Merge branch 'master' into msw_incineration
This commit is contained in:
lisazeyen
GitHub
commit
648ea20ec1
@ -2,8 +2,325 @@
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#
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# SPDX-License-Identifier: CC0-1.0
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# Exclude pre-commit applications
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# Note
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# Needs to be setup via
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# git config blame.ignoreRevsFile .git-blame-ignore-revs
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# Custom commits
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# pre-commit commits
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3343394c65e4268b0c375fd3024daff9d6f94c60
|
||||
7d6d6d2805f34a6de6d6e1400d376e8970886c4d
|
||||
b21965a98600b4fcd3bbf286ac9b5f9bd9032fa6
|
||||
399907c1f4e65ea00f53f1050df6117c75f71e8c
|
||||
c431cf6f44a221dc6e7971d4977a1575c6d16867
|
||||
e18b188e81f64656b45232da8ec15c9954ef2281
|
||||
b5f5b35f41208af238d6f6bc2bdb2a4fd1f71110
|
||||
f0453c42c0266468bc3cc0dfe1bf527f778dff2d
|
||||
817e82a80529dd34254643b9786a806eb2b6769c
|
||||
93d79da903c10ff51ebd1db494165ca99aa4c5ae
|
||||
4f890799455b11ccd671ebe0785e32d2c7d34d76
|
||||
f6a40d36969b24c49b64c912f46f7d33c16cfd99
|
||||
552f9b8bd3233ae4d6a123e04e958a99dee8748e
|
||||
e8772c33401d8e5ba4290678d820b18004f0d6d4
|
||||
add135fe0527c2bf29cbfcbcf1639931fee057bb
|
||||
bdeab82b494d2b7edd1700b5339f7ce3cbe89fa6
|
||||
70b8ec7e44ae11e2a9b0f24491f60fbf05429a36
|
||||
699a4bd2e8e09035e3b93e0d117ba6874e119a8e
|
||||
4d8f9390047b7f3dbb1836ad4390c74a33c6632d
|
||||
94d41b262ab0008ca0110606dac32e418b22f1df
|
||||
9ae7a93ccb01bcb3570ef7e2c3120ab725720245
|
||||
aa5d4b0c90c0292b60421e1944f994b91cfec524
|
||||
74e9d56adb1ea4c71fdaa764efbb9e69a363e8af
|
||||
2433c00dcba8162b8068fbfa2e2029e60646b560
|
||||
3625d401c619e5f6cf86efcc8cf1263f0a7a1ffe
|
||||
0a3c177f4b6083a00f75810340cf7570f373bb15
|
||||
51785524a3e28db65d6444777a706dcf144efc1c
|
||||
05b0a81808c63de36cc90fef489225160522293e
|
||||
763d77d19d0614eed198c23bc97338eebf61995c
|
||||
460bbd080f5fd119abacd2cf64c2946dcfd4231c
|
||||
5f554ab28fa326ddb140a655733d6adcd9a3cc65
|
||||
423c3d69992bd803d17f747e18c72a054e4da474
|
||||
cae7d2db30eeaeb61b847f35e6178b72b69224e5
|
||||
e261c5da31e2f27d2e9538ec3bd785b3d98b233a
|
||||
58eab3cf2f9b05ba26a8506ef6c8a90871ee3db6
|
||||
e83b1b029128c392c6ff9e5c9de30e5ce0f1e141
|
||||
7cff6b5e1d8b02014979e3866fb040afa955d7b0
|
||||
435732021833ab48172b3005852a1eeb88af4af5
|
||||
c24b119928b4b2475f578c9b7f282381bf95ee53
|
||||
23c92ca436575bff821d8f15b340a6bf94020596
|
||||
36fbc53289da6783d34a61cd0860ce9aef497aa2
|
||||
a9b09e4ae4fa03725575c6fcf3ab2ad3a81c992d
|
||||
3a80ac20277e0d3d72825e88a4872af57f4401af
|
||||
ce6df71399c9b7175736c2666a5ec94bf3c61291
|
||||
340290883434116288b977febdfb0cd5ef408662
|
||||
f128cc3e3cfddad4659f3a7b5bd3f53b640f68e1
|
||||
f717ce9d3d691ff35849bbe62d9264f9f0c66426
|
||||
d45bee628e767b1f28f73f9f16b6bdabda2ad40d
|
||||
54f0cde4907b6451d289c32a6ff15d3d007095dd
|
||||
0b57626387a0a61e764de5413362e5389f1b71c2
|
||||
27c0d3a2d2988d8e01d3e8d6e14793a7ee5ed843
|
||||
8fa29865b6365620513f9a30c7e14b31a9ec1c7a
|
||||
b74b4239fa387b0222654df93590960e6beb1bee
|
||||
78a580ddf1ab885006020a9313e66a21534455af
|
||||
b7c58883b7cf2279f638c450e12bfa920221d785
|
||||
9026e0920dc932a0705de81b484eea21bb08abe7
|
||||
8db3780fce367aa9643d54eea7ca3e89646273cb
|
||||
7c7da754bb91acfe6f3eb098cac36239ce908319
|
||||
e65ac95a44a5d5199105aa8dd190ebe6657d998b
|
||||
d0e0880b1965a6769197be1fc9aaa5650e229bca
|
||||
5ab10eae37cd71a4676478c79c79b520ff048d61
|
||||
13769f90af4500948b0376d57df4cceaa13e78b5
|
||||
36003c96270f622e67288ed8179e04cbbaa42d84
|
||||
b134a395b44d1023f1c9f5cf63e2feffa1faf9c0
|
||||
64745e7ec2ef7008d128d9ef2234f5d78a2243b8
|
||||
03db47ed5e4b1e02a784dbf0274627ecb0714b73
|
||||
9f87099dbbb0d2519235cc19327dbb198776e040
|
||||
cb94e5974eddb705f5391cee0fea489e5f573fbb
|
||||
e6ecbc95d71a47e913680db14f3861daad4dae11
|
||||
8bdba5653a51e42ac1651b8def36f1b4a3a0999a
|
||||
0cf6d47afbac9d273f27e37f84592c5fb4b430db
|
||||
3c099095dc50ce7d27d080686b005b49248cc216
|
||||
acc6ee6bfe25114e9527c3f27de1307ff098490a
|
||||
94e5f160b0f46764c4c95eed6a7de90ef3d65717
|
||||
dcd16e32a88385fb1fe8366da4de9807ce33baf3
|
||||
85d01bceb0dd87fb0d02648cd70d753450175f62
|
||||
92080b1cd2ca5f123158571481722767b99c2b27
|
||||
5d1ef8a64055a039aa4a0834d2d26fe7752fe9a0
|
||||
|
||||
39
.github/workflows/update-fixed-env.yaml
vendored
Normal file
39
.github/workflows/update-fixed-env.yaml
vendored
Normal file
@ -0,0 +1,39 @@
|
||||
name: Fixed Environment YAML Monitor
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- master
|
||||
paths:
|
||||
- 'env/environment.yaml'
|
||||
|
||||
jobs:
|
||||
update_environment_fixed:
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
steps:
|
||||
- name: Checkout Repository
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Setup micromamba
|
||||
uses: mamba-org/setup-micromamba@v1
|
||||
with:
|
||||
micromamba-version: latest
|
||||
environment-file: envs/environment.yaml
|
||||
log-level: debug
|
||||
init-shell: bash
|
||||
cache-environment: true
|
||||
cache-downloads: true
|
||||
|
||||
- name: Update environment.fixed.yaml
|
||||
run: |
|
||||
mamba env export --file envs/environment.fixed.yaml --no-builds
|
||||
|
||||
- name: Create Pull Request
|
||||
uses: peter-evans/create-pull-request@v6
|
||||
with:
|
||||
token: ${{ secrets.GITHUB_TOKEN }}
|
||||
branch: update-environment-fixed
|
||||
title: Update fixed environment
|
||||
body: Automatically generated PR to update environment.fixed.yaml
|
||||
labels: automated
|
||||
@ -67,7 +67,7 @@ repos:
|
||||
|
||||
# Do YAML formatting (before the linter checks it for misses)
|
||||
- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
|
||||
rev: v2.13.0
|
||||
rev: v2.14.0
|
||||
hooks:
|
||||
- id: pretty-format-yaml
|
||||
args: [--autofix, --indent, "2", --preserve-quotes]
|
||||
@ -87,6 +87,6 @@ repos:
|
||||
|
||||
# Check for FSFE REUSE compliance (licensing)
|
||||
- repo: https://github.com/fsfe/reuse-tool
|
||||
rev: v3.1.0a1
|
||||
rev: v4.0.3
|
||||
hooks:
|
||||
- id: reuse
|
||||
|
||||
@ -65,10 +65,10 @@ The dataset consists of:
|
||||
(alternating current lines at and above 220kV voltage level and all high
|
||||
voltage direct current lines) and 3803 substations.
|
||||
- The open power plant database
|
||||
[powerplantmatching](https://github.com/FRESNA/powerplantmatching).
|
||||
[powerplantmatching](https://github.com/PyPSA/powerplantmatching).
|
||||
- Electrical demand time series from the
|
||||
[OPSD project](https://open-power-system-data.org/).
|
||||
- Renewable time series based on ERA5 and SARAH, assembled using the [atlite tool](https://github.com/FRESNA/atlite).
|
||||
- Renewable time series based on ERA5 and SARAH, assembled using the [atlite tool](https://github.com/PyPSA/atlite).
|
||||
- Geographical potentials for wind and solar generators based on land use (CORINE) and excluding nature reserves (Natura2000) are computed with the [atlite library](https://github.com/PyPSA/atlite).
|
||||
|
||||
A sector-coupled extension adds demand
|
||||
|
||||
@ -134,35 +134,25 @@ electricity:
|
||||
|
||||
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#atlite
|
||||
atlite:
|
||||
default_cutout: europe-2013-era5
|
||||
default_cutout: europe-2013-sarah3-era5
|
||||
nprocesses: 4
|
||||
show_progress: false
|
||||
cutouts:
|
||||
# use 'base' to determine geographical bounds and time span from config
|
||||
# base:
|
||||
# module: era5
|
||||
europe-2013-era5:
|
||||
module: era5 # in priority order
|
||||
europe-2013-sarah3-era5:
|
||||
module: [sarah, era5] # in priority order
|
||||
x: [-12., 42.]
|
||||
y: [33., 72]
|
||||
y: [33., 72.]
|
||||
dx: 0.3
|
||||
dy: 0.3
|
||||
time: ['2013', '2013']
|
||||
europe-2013-sarah:
|
||||
module: [sarah, era5] # in priority order
|
||||
x: [-12., 42.]
|
||||
y: [33., 65]
|
||||
dx: 0.2
|
||||
dy: 0.2
|
||||
time: ['2013', '2013']
|
||||
sarah_interpolate: false
|
||||
sarah_dir:
|
||||
features: [influx, temperature]
|
||||
|
||||
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#renewable
|
||||
renewable:
|
||||
onwind:
|
||||
cutout: europe-2013-era5
|
||||
cutout: europe-2013-sarah3-era5
|
||||
resource:
|
||||
method: wind
|
||||
turbine: Vestas_V112_3MW
|
||||
@ -181,7 +171,7 @@ renewable:
|
||||
excluder_resolution: 100
|
||||
clip_p_max_pu: 1.e-2
|
||||
offwind-ac:
|
||||
cutout: europe-2013-era5
|
||||
cutout: europe-2013-sarah3-era5
|
||||
resource:
|
||||
method: wind
|
||||
turbine: NREL_ReferenceTurbine_2020ATB_5.5MW
|
||||
@ -197,7 +187,7 @@ renewable:
|
||||
excluder_resolution: 200
|
||||
clip_p_max_pu: 1.e-2
|
||||
offwind-dc:
|
||||
cutout: europe-2013-era5
|
||||
cutout: europe-2013-sarah3-era5
|
||||
resource:
|
||||
method: wind
|
||||
turbine: NREL_ReferenceTurbine_2020ATB_5.5MW
|
||||
@ -213,7 +203,7 @@ renewable:
|
||||
excluder_resolution: 200
|
||||
clip_p_max_pu: 1.e-2
|
||||
offwind-float:
|
||||
cutout: europe-2013-era5
|
||||
cutout: europe-2013-sarah3-era5
|
||||
resource:
|
||||
method: wind
|
||||
turbine: NREL_ReferenceTurbine_5MW_offshore
|
||||
@ -231,7 +221,7 @@ renewable:
|
||||
max_depth: 1000
|
||||
clip_p_max_pu: 1.e-2
|
||||
solar:
|
||||
cutout: europe-2013-sarah
|
||||
cutout: europe-2013-sarah3-era5
|
||||
resource:
|
||||
method: pv
|
||||
panel: CSi
|
||||
@ -246,7 +236,7 @@ renewable:
|
||||
excluder_resolution: 100
|
||||
clip_p_max_pu: 1.e-2
|
||||
solar-hsat:
|
||||
cutout: europe-2013-sarah
|
||||
cutout: europe-2013-sarah3-era5
|
||||
resource:
|
||||
method: pv
|
||||
panel: CSi
|
||||
@ -261,7 +251,7 @@ renewable:
|
||||
excluder_resolution: 100
|
||||
clip_p_max_pu: 1.e-2
|
||||
hydro:
|
||||
cutout: europe-2013-era5
|
||||
cutout: europe-2013-sarah3-era5
|
||||
carriers: [ror, PHS, hydro]
|
||||
PHS_max_hours: 6
|
||||
hydro_max_hours: "energy_capacity_totals_by_country" # one of energy_capacity_totals_by_country, estimate_by_large_installations or a float
|
||||
@ -295,7 +285,7 @@ lines:
|
||||
under_construction: 'keep' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
|
||||
dynamic_line_rating:
|
||||
activate: false
|
||||
cutout: europe-2013-era5
|
||||
cutout: europe-2013-sarah3-era5
|
||||
correction_factor: 0.95
|
||||
max_voltage_difference: false
|
||||
max_line_rating: false
|
||||
@ -408,6 +398,7 @@ sector:
|
||||
biomass: true
|
||||
industry: true
|
||||
agriculture: true
|
||||
fossil_fuels: true
|
||||
district_heating:
|
||||
potential: 0.6
|
||||
progress:
|
||||
@ -572,12 +563,12 @@ sector:
|
||||
min_part_load_fischer_tropsch: 0.5
|
||||
min_part_load_methanolisation: 0.3
|
||||
min_part_load_methanation: 0.3
|
||||
use_fischer_tropsch_waste_heat: true
|
||||
use_haber_bosch_waste_heat: true
|
||||
use_methanolisation_waste_heat: true
|
||||
use_methanation_waste_heat: true
|
||||
use_fuel_cell_waste_heat: true
|
||||
use_electrolysis_waste_heat: true
|
||||
use_fischer_tropsch_waste_heat: 0.25
|
||||
use_haber_bosch_waste_heat: 0.25
|
||||
use_methanolisation_waste_heat: 0.25
|
||||
use_methanation_waste_heat: 0.25
|
||||
use_fuel_cell_waste_heat: 0.25
|
||||
use_electrolysis_waste_heat: 0.25
|
||||
electricity_transmission_grid: true
|
||||
electricity_distribution_grid: true
|
||||
electricity_distribution_grid_cost_factor: 1.0
|
||||
@ -787,6 +778,7 @@ solving:
|
||||
options:
|
||||
clip_p_max_pu: 1.e-2
|
||||
load_shedding: false
|
||||
curtailment_mode: false
|
||||
noisy_costs: true
|
||||
skip_iterations: true
|
||||
rolling_horizon: false
|
||||
@ -831,7 +823,7 @@ solving:
|
||||
solver_options:
|
||||
highs-default:
|
||||
# refer to https://ergo-code.github.io/HiGHS/dev/options/definitions/
|
||||
threads: 4
|
||||
threads: 1
|
||||
solver: "ipm"
|
||||
run_crossover: "off"
|
||||
small_matrix_value: 1e-6
|
||||
@ -842,7 +834,7 @@ solving:
|
||||
parallel: "on"
|
||||
random_seed: 123
|
||||
gurobi-default:
|
||||
threads: 4
|
||||
threads: 8
|
||||
method: 2 # barrier
|
||||
crossover: 0
|
||||
BarConvTol: 1.e-6
|
||||
@ -880,6 +872,13 @@ solving:
|
||||
Threads: 8
|
||||
LpMethod: 2
|
||||
Crossover: 0
|
||||
RelGap: 1.e-6
|
||||
Dualize: 0
|
||||
copt-gpu:
|
||||
LpMethod: 6
|
||||
GPUMode: 1
|
||||
PDLPTol: 1.e-5
|
||||
Crossover: 0
|
||||
cbc-default: {} # Used in CI
|
||||
glpk-default: {} # Used in CI
|
||||
|
||||
@ -1058,7 +1057,7 @@ plotting:
|
||||
V2G: '#e5ffa8'
|
||||
land transport EV: '#baf238'
|
||||
land transport demand: '#38baf2'
|
||||
Li ion: '#baf238'
|
||||
EV battery: '#baf238'
|
||||
# hot water storage
|
||||
water tanks: '#e69487'
|
||||
residential rural water tanks: '#f7b7a3'
|
||||
|
||||
@ -1,151 +0,0 @@
|
||||
name,GDP_PPP,country
|
||||
3140,632728.0438507323,MD
|
||||
3139,806541.9318093687,MD
|
||||
3142,1392454.6690911907,MD
|
||||
3152,897871.2903553953,MD
|
||||
3246,645554.8588933202,MD
|
||||
7049,1150156.4449477682,MD
|
||||
1924,162285.16792916053,UA
|
||||
1970,751970.6071848695,UA
|
||||
2974,368873.75840156944,UA
|
||||
2977,294847.85539198935,UA
|
||||
2979,197988.13680768458,UA
|
||||
2980,301371.2491126519,UA
|
||||
3031,56925.21878805953,UA
|
||||
3032,139395.18279351242,UA
|
||||
3033,145377.8061037629,UA
|
||||
3035,52282.83655208812,UA
|
||||
3036,497950.25890516065,UA
|
||||
3037,1183293.1987702171,UA
|
||||
3038,255005.98207636533,UA
|
||||
3039,224711.50098325178,UA
|
||||
3040,342959.943226467,UA
|
||||
3044,69119.31486955672,UA
|
||||
3045,246273.65986119965,UA
|
||||
3047,146742.08407299497,UA
|
||||
3049,107265.7028733467,UA
|
||||
3050,1126147.985259493,UA
|
||||
3051,69833.56303043803,UA
|
||||
3052,67230.88206577855,UA
|
||||
3053,27019.224685201345,UA
|
||||
3054,260571.47337292184,UA
|
||||
3055,88760.94152915622,UA
|
||||
3056,101368.26196568517,UA
|
||||
3058,55752.92329667119,UA
|
||||
3059,89024.37880630122,UA
|
||||
3062,358411.291265149,UA
|
||||
3064,75081.64142862396,UA
|
||||
3065,158101.42949135564,UA
|
||||
3066,83763.89576442329,UA
|
||||
3068,173474.51218344545,UA
|
||||
3069,60327.01572375589,UA
|
||||
3070,18073.687271955278,UA
|
||||
3071,249069.43314695224,UA
|
||||
3072,220707.35700825177,UA
|
||||
3073,61342.30137462664,UA
|
||||
3074,254235.98867635374,UA
|
||||
3077,769558.9832370486,UA
|
||||
3078,132674.2315809836,UA
|
||||
3079,1388517.1478032232,UA
|
||||
3080,1861003.8718246964,UA
|
||||
3082,140123.73854745473,UA
|
||||
3083,834887.5595419679,UA
|
||||
3084,1910795.5590558557,UA
|
||||
3086,93828.36549170096,UA
|
||||
3088,347197.65113392205,UA
|
||||
3089,3754718.141734592,UA
|
||||
3090,521912.69768585655,UA
|
||||
3093,232818.05269714879,UA
|
||||
3095,435376.20361377904,UA
|
||||
3099,345596.5288937008,UA
|
||||
3100,175689.10947424968,UA
|
||||
3105,538438.9311459162,UA
|
||||
3107,88096.86032871014,UA
|
||||
3108,79847.68447063807,UA
|
||||
3109,348504.73449373,UA
|
||||
3144,71657.0165675802,UA
|
||||
3146,80342.05037424155,UA
|
||||
3158,74465.12922576343,UA
|
||||
3164,3102112.2672631275,UA
|
||||
3165,65215.04081671433,UA
|
||||
3166,413924.2225725632,UA
|
||||
3167,135060.0056434935,UA
|
||||
3168,54980.442979330146,UA
|
||||
3170,29584.879122227037,UA
|
||||
3171,142780.68163047134,UA
|
||||
3172,40436.63814695243,UA
|
||||
3173,1253342.1790126422,UA
|
||||
3174,173842.03139155387,UA
|
||||
3176,65699.76352408895,UA
|
||||
3177,143591.75419817626,UA
|
||||
3178,56434.04525832523,UA
|
||||
3179,389996.1670051216,UA
|
||||
3180,138452.84503524794,UA
|
||||
3181,67402.59500436619,UA
|
||||
3184,51204.293695376415,UA
|
||||
3185,46867.82356528432,UA
|
||||
3186,103892.35612417295,UA
|
||||
3187,193668.91476930346,UA
|
||||
3189,54584.176457692694,UA
|
||||
3190,219077.64942830536,UA
|
||||
3197,88516.52699983507,UA
|
||||
3198,298166.8272673622,UA
|
||||
3199,61334.952541812374,UA
|
||||
3229,175692.61136747137,UA
|
||||
3230,106722.62773321665,UA
|
||||
3236,61542.06264321315,UA
|
||||
3241,83752.90489164277,UA
|
||||
4301,48419.52825967164,UA
|
||||
4305,147759.74280349456,UA
|
||||
4306,53156.905740992224,UA
|
||||
4315,218025.78516351627,UA
|
||||
4317,155240.40554731718,UA
|
||||
4318,1342144.2459407183,UA
|
||||
4319,91669.1449633853,UA
|
||||
4321,85852.49282415409,UA
|
||||
4347,67938.7698430624,UA
|
||||
4357,20064.979012172935,UA
|
||||
4360,47840.51245168512,UA
|
||||
4361,55580.924388032574,UA
|
||||
4362,165753.82588729708,UA
|
||||
4363,46390.2448142152,UA
|
||||
4365,96265.47592938849,UA
|
||||
4366,272003.25510057947,UA
|
||||
4367,80878.50229245829,UA
|
||||
4370,330072.35444044066,UA
|
||||
4371,7707066.181975477,UA
|
||||
4373,2019766.7891575783,UA
|
||||
4374,985354.331818515,UA
|
||||
4377,230805.08833664874,UA
|
||||
4382,125670.67125287943,UA
|
||||
4383,46914.065511740075,UA
|
||||
4384,48020.804310510954,UA
|
||||
4385,55612.34707641123,UA
|
||||
4387,74558.3475791577,UA
|
||||
4388,245243.33449409154,UA
|
||||
4389,95696.56767732685,UA
|
||||
4391,251085.7523045193,UA
|
||||
4401,66375.82996856027,UA
|
||||
4403,111954.41038437477,UA
|
||||
4405,46911.68560148837,UA
|
||||
4408,150782.51691456966,UA
|
||||
4409,112776.7399582134,UA
|
||||
4410,153076.56860965435,UA
|
||||
4412,192629.31238456024,UA
|
||||
4413,181295.3120834606,UA
|
||||
4414,995694.9413199169,UA
|
||||
4416,157640.7868989174,UA
|
||||
4418,77580.20674809469,UA
|
||||
4420,122320.99275223716,UA
|
||||
4424,184891.10924920067,UA
|
||||
4425,84486.75974340564,UA
|
||||
4431,50485.84380961137,UA
|
||||
4435,231040.45446464577,UA
|
||||
4436,81222.18707585508,UA
|
||||
4438,114819.76472988473,UA
|
||||
4439,76839.1052178896,UA
|
||||
4440,135337.0313562152,UA
|
||||
4441,49159.485269198034,UA
|
||||
7031,42001.73757065917,UA
|
||||
7059,159790.48382874,UA
|
||||
7063,39599.10564971086,UA
|
||||
|
@ -3,7 +3,7 @@ default_cutout,--,str,"Defines a default cutout."
|
||||
nprocesses,--,int,"Number of parallel processes in cutout preparation"
|
||||
show_progress,bool,true/false,"Whether progressbar for atlite conversion processes should be shown. False saves time."
|
||||
cutouts,,,
|
||||
-- {name},--,"Convention is to name cutouts like ``<region>-<year>-<source>`` (e.g. ``europe-2013-era5``).","Name of the cutout netcdf file. The user may specify multiple cutouts under configuration ``atlite: cutouts:``. Reference is used in configuration ``renewable: {technology}: cutout:``. The cutout ``base`` may be used to automatically calculate temporal and spatial bounds of the network."
|
||||
-- {name},--,"Convention is to name cutouts like ``<region>-<year>-<source>`` (e.g. ``europe-2013-sarah3-era5``).","Name of the cutout netcdf file. The user may specify multiple cutouts under configuration ``atlite: cutouts:``. Reference is used in configuration ``renewable: {technology}: cutout:``. The cutout ``base`` may be used to automatically calculate temporal and spatial bounds of the network."
|
||||
-- -- module,--,"Subset of {'era5','sarah'}","Source of the reanalysis weather dataset (e.g. `ERA5 <https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5>`_ or `SARAH-2 <https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002>`_)"
|
||||
-- -- x,°,"Float interval within [-180, 180]","Range of longitudes to download weather data for. If not defined, it defaults to the spatial bounds of all bus shapes."
|
||||
-- -- y,°,"Float interval within [-90, 90]","Range of latitudes to download weather data for. If not defined, it defaults to the spatial bounds of all bus shapes."
|
||||
|
||||
|
@ -1,5 +1,5 @@
|
||||
,Unit,Values,Description
|
||||
cutout,--,Must be 'europe-2013-era5',Specifies the directory where the relevant weather data ist stored.
|
||||
cutout,--,Must be 'europe-2013-sarah3-era5',Specifies the directory where the relevant weather data ist stored.
|
||||
carriers,--,"Any subset of {'ror', 'PHS', 'hydro'}","Specifies the types of hydro power plants to build per-unit availability time series for. 'ror' stands for run-of-river plants, 'PHS' represents pumped-hydro storage, and 'hydro' stands for hydroelectric dams."
|
||||
PHS_max_hours,h,float,Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity ``p_nom``. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
|
||||
hydro_max_hours,h,"Any of {float, 'energy_capacity_totals_by_country', 'estimate_by_large_installations'}",Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity ``p_nom`` or heuristically determined. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
|
||||
|
||||
|
@ -8,7 +8,7 @@ under_construction,--,"One of {'zero': set capacity to zero, 'remove': remove co
|
||||
reconnect_crimea,--,"true or false","Whether to reconnect Crimea to the Ukrainian grid"
|
||||
dynamic_line_rating,,,
|
||||
-- activate,bool,"true or false","Whether to take dynamic line rating into account"
|
||||
-- cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
|
||||
-- cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-sarah3-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
|
||||
-- correction_factor,--,"float","Factor to compensate for overestimation of wind speeds in hourly averaged wind data"
|
||||
-- max_voltage_difference,deg,"float","Maximum voltage angle difference in degrees or 'false' to disable"
|
||||
-- max_line_rating,--,"float","Maximum line rating relative to nominal capacity without DLR, e.g. 1.3 or 'false' to disable"
|
||||
|
||||
|
@ -1,5 +1,5 @@
|
||||
,Unit,Values,Description
|
||||
cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
|
||||
cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-sarah3-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
|
||||
resource,,,
|
||||
-- method,--,"Must be 'wind'","A superordinate technology type."
|
||||
-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the turbine type and its characteristic power curve."
|
||||
|
||||
|
@ -1,5 +1,5 @@
|
||||
,Unit,Values,Description
|
||||
cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
|
||||
cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-sarah3-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
|
||||
resource,,,
|
||||
-- method,--,"Must be 'wind'","A superordinate technology type."
|
||||
-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the turbine type and its characteristic power curve."
|
||||
|
||||
|
@ -1,5 +1,5 @@
|
||||
,Unit,Values,Description
|
||||
cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
|
||||
cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-sarah3-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
|
||||
resource,,,
|
||||
-- method,--,"Must be 'wind'","A superordinate technology type."
|
||||
-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the turbine type and its characteristic power curve."
|
||||
|
||||
|
@ -4,8 +4,9 @@ heating,--,"{true, false}",Flag to include heating sector.
|
||||
biomass,--,"{true, false}",Flag to include biomass sector.
|
||||
industry,--,"{true, false}",Flag to include industry sector.
|
||||
agriculture,--,"{true, false}",Flag to include agriculture sector.
|
||||
fossil_fuels,--,"{true, false}","Flag to include imports of fossil fuels ( [""coal"", ""gas"", ""oil"", ""lignite""])"
|
||||
district_heating,--,,`prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_
|
||||
-- potential,--,float,maximum fraction of urban demand which can be supplied by district heating
|
||||
-- potential,--,float,maximum fraction of urban demand which can be supplied by district heating. Ignored where below current fraction.
|
||||
-- progress,--,Dictionary with planning horizons as keys., Increase of today's district heating demand to potential maximum district heating share. Progress = 0 means today's district heating share. Progress = 1 means maximum fraction of urban demand is supplied by district heating
|
||||
-- district_heating_loss,--,float,Share increase in district heat demand in urban central due to heat losses
|
||||
cluster_heat_buses,--,"{true, false}",Cluster residential and service heat buses in `prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_ to one to save memory.
|
||||
@ -71,7 +72,7 @@ boilers,--,"{true, false}",Add option for transforming gas into heat using gas b
|
||||
resistive_heaters,--,"{true, false}",Add option for transforming electricity into heat using resistive heaters (independently from gas boilers)
|
||||
oil_boilers,--,"{true, false}",Add option for transforming oil into heat using boilers
|
||||
biomass_boiler,--,"{true, false}",Add option for transforming biomass into heat using boilers
|
||||
overdimension_individual_heating,--,"float",Add option for overdimensioning individual heating systems by a certain factor. This allows them to cover heat demand peaks e.g. 10% higher than those in the data with a setting of 1.1.
|
||||
overdimension_individual_heating,--,float,Add option for overdimensioning individual heating systems by a certain factor. This allows them to cover heat demand peaks e.g. 10% higher than those in the data with a setting of 1.1.
|
||||
chp,--,"{true, false}",Add option for using Combined Heat and Power (CHP)
|
||||
micro_chp,--,"{true, false}",Add option for using Combined Heat and Power (CHP) for decentral areas.
|
||||
solar_thermal,--,"{true, false}",Add option for using solar thermal to generate heat.
|
||||
|
||||
|
@ -1,5 +1,5 @@
|
||||
,Unit,Values,Description
|
||||
cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module can be ERA5 or SARAH-2.","Specifies the directory where the relevant weather data ist stored that is specified at ``atlite/cutouts`` configuration. Both ``sarah`` and ``era5`` work."
|
||||
cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-sarah3-era5') or reference an existing folder in the directory ``cutouts``. Source module can be ERA5 or SARAH-2.","Specifies the directory where the relevant weather data ist stored that is specified at ``atlite/cutouts`` configuration. Both ``sarah`` and ``era5`` work."
|
||||
resource,,,
|
||||
-- method,--,"Must be 'pv'","A superordinate technology type."
|
||||
-- panel,--,"One of {'Csi', 'CdTe', 'KANENA'} as defined in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/solarpanel>`_ . Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the solar panel technology and its characteristic attributes."
|
||||
|
||||
|
@ -2,6 +2,7 @@
|
||||
options,,,
|
||||
-- clip_p_max_pu,p.u.,float,To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero.
|
||||
-- load_shedding,bool/float,"{'true','false', float}","Add generators with very high marginal cost to simulate load shedding and avoid problem infeasibilities. If load shedding is a float, it denotes the marginal cost in EUR/kWh."
|
||||
-- curtailment_mode,bool/float,"{'true','false'}","Fixes the dispatch profiles of generators with time-varying p_max_pu by setting ``p_min_pu = p_max_pu`` and adds an auxiliary curtailment generator (with negative sign to absorb excess power) at every AC bus. This can speed up the solving process as the curtailment decision is aggregated into a single generator per region. Defaults to ``false``."
|
||||
-- noisy_costs,bool,"{'true','false'}","Add random noise to marginal cost of generators by :math:`\mathcal{U}(0.009,0,011)` and capital cost of lines and links by :math:`\mathcal{U}(0.09,0,11)`."
|
||||
-- skip_iterations,bool,"{'true','false'}","Skip iterating, do not update impedances of branches. Defaults to true."
|
||||
-- rolling_horizon,bool,"{'true','false'}","Switch for rule :mod:`solve_operations_network` whether to optimize the network in a rolling horizon manner, where the snapshot range is split into slices of size `horizon` which are solved consecutively. This setting has currently no effect on sector-coupled networks."
|
||||
|
||||
|
@ -242,7 +242,7 @@ Rule overview
|
||||
file
|
||||
<https://pypsa-eur.readthedocs.io/en/latest/preparation/build_powerplants.html?highlight=powerplants>`__
|
||||
generated by pypsa-eur which, in turn, is based on the `powerplantmatching
|
||||
<https://github.com/FRESNA/powerplantmatching>`__ database.
|
||||
<https://github.com/PyPSA/powerplantmatching>`__ database.
|
||||
|
||||
Existing wind and solar capacities are retrieved from `IRENA annual statistics
|
||||
<https://www.irena.org/Statistics/Download-Data>`__ and distributed among the
|
||||
|
||||
@ -81,7 +81,8 @@ Nevertheless, you can still use open-source solvers for smaller problems.
|
||||
.. note::
|
||||
The rules :mod:`cluster_network` and :mod:`simplify_network` solve a mixed-integer quadratic optimisation problem for clustering.
|
||||
The open-source solvers HiGHS, Cbc and GlPK cannot handle this. A fallback to SCIP is implemented in this case, which is included in the standard environment specifications.
|
||||
For an open-source solver setup install in your ``conda`` environment on OSX/Linux. To install the default solver Gurobi, run
|
||||
For an open-source solver setup install for example HiGHS **and** SCIP in your ``conda`` environment on OSX/Linux.
|
||||
To install the default solver Gurobi, run
|
||||
|
||||
.. code:: bash
|
||||
|
||||
|
||||
@ -16,7 +16,7 @@ using the ``retrieve*`` rules (:ref:`data`).
|
||||
Having downloaded the necessary data,
|
||||
|
||||
- :mod:`build_shapes` generates GeoJSON files with shapes of the countries, exclusive economic zones and `NUTS3 <https://en.wikipedia.org/wiki/Nomenclature_of_Territorial_Units_for_Statistics>`__ areas.
|
||||
- :mod:`build_cutout` prepares smaller weather data portions from `ERA5 <https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5>`__ for cutout ``europe-2013-era5`` and SARAH for cutout ``europe-2013-sarah``.
|
||||
- :mod:`build_cutout` prepares smaller weather data portions from `ERA5 <https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5>`__ for cutout ``europe-2013-sarah3-era5`` and SARAH for cutout ``europe-2013-sarah``.
|
||||
|
||||
With these and the externally extracted ENTSO-E online map topology
|
||||
(``data/entsoegridkit``), it can build a base PyPSA network with the following rules:
|
||||
@ -25,7 +25,7 @@ With these and the externally extracted ENTSO-E online map topology
|
||||
|
||||
Then the process continues by calculating conventional power plant capacities, potentials, and per-unit availability time series for variable renewable energy carriers and hydro power plants with the following rules:
|
||||
|
||||
- :mod:`build_powerplants` for today's thermal power plant capacities using `powerplantmatching <https://github.com/FRESNA/powerplantmatching>`__ allocating these to the closest substation for each powerplant,
|
||||
- :mod:`build_powerplants` for today's thermal power plant capacities using `powerplantmatching <https://github.com/PyPSA/powerplantmatching>`__ allocating these to the closest substation for each powerplant,
|
||||
- :mod:`build_ship_raster` for building shipping traffic density,
|
||||
- :mod:`build_renewable_profiles` for the hourly capacity factors and installation potentials constrained by land-use in each substation's Voronoi cell for PV, onshore and offshore wind, and
|
||||
- :mod:`build_hydro_profile` for the hourly per-unit hydro power availability time series.
|
||||
|
||||
@ -10,6 +10,16 @@ Release Notes
|
||||
Upcoming Release
|
||||
================
|
||||
|
||||
* Add flag ``sector: fossil_fuels`` in config to remove the option of importing fossil fuels
|
||||
|
||||
* Renamed the carrier of batteries in BEVs from `battery storage` to `EV battery` and the corresponding bus carrier from `Li ion` to `EV battery`. This is to avoid confusion with stationary battery storage.
|
||||
|
||||
* Changed default assumptions about waste heat usage from PtX and fuel cells in district heating.
|
||||
The default value for the link efficiency scaling factor was changed from 100% to 25%.
|
||||
It can be set to other values in the configuration ``sector: use_TECHNOLOGY_waste_heat``.
|
||||
|
||||
* In simplifying polygons in :mod:`build_shapes` default to no tolerance.
|
||||
|
||||
* Set non-zero capital_cost for methanol stores to avoid unrealistic storage sizes
|
||||
|
||||
* Set p_nom = p_nom_min for generators with baseyear == grouping_year in add_existing_baseyear. This has no effect on the optimization but helps n.statistics to correctly report already installed capacities.
|
||||
@ -25,6 +35,33 @@ Upcoming Release
|
||||
|
||||
* Bugfix: Correctly read in threshold capacity below which to remove components from previous planning horizons in :mod:`add_brownfield`.
|
||||
|
||||
* For countries not contained in the NUTS3-specific datasets (i.e. MD and UA), the mapping of GDP per capita and population per bus region used to spatially distribute electricity demand is now endogenised in a new rule :mod:`build_gdp_ppp_non_nuts3`. https://github.com/PyPSA/pypsa-eur/pull/1146
|
||||
|
||||
* The databundle has been updated to release v0.3.0, which includes raw GDP and population data for countries outside the NUTS system (UA, MD). https://github.com/PyPSA/pypsa-eur/pull/1146
|
||||
|
||||
* Updated filtering in :mod:`determine_availability_matrix_MD_UA.py` to improve speed. https://github.com/PyPSA/pypsa-eur/pull/1146
|
||||
|
||||
* Bugfix: Impose minimum value of zero for district heating progress between current and future market share in :mod:`build_district_heat_share`.
|
||||
|
||||
* The ``{scope}`` wildcard was removed, since its outputs were not used.
|
||||
|
||||
* Enable parallelism in :mod:`determine_availability_matrix_MD_UA.py` and remove plots. This requires the use of temporary files.
|
||||
|
||||
* Updated pre-built `weather data cutouts
|
||||
<https://zenodo.org/records/12791128>`__. These are now merged cutouts with
|
||||
solar irradiation from the new SARAH-3 dataset while taking all other
|
||||
variables from ERA5. Cutouts are now available for multiple years (2010, 2013,
|
||||
2019, and 2023).
|
||||
|
||||
* Added option ``solving: curtailment_mode``` which fixes the dispatch profiles
|
||||
of generators with time-varying p_max_pu by setting ``p_min_pu = p_max_pu``
|
||||
and adds an auxiliary curtailment generator with negative sign (to absorb
|
||||
excess power) at every AC bus. This can speed up the solving process as the
|
||||
curtailment decision is aggregated into a single generator per region.
|
||||
|
||||
* In :mod:`base_network`, replace own voronoi polygon calculation function with
|
||||
Geopandas `gdf.voronoi_polygons` method.
|
||||
|
||||
PyPSA-Eur 0.11.0 (25th May 2024)
|
||||
=====================================
|
||||
|
||||
|
||||
@ -142,13 +142,6 @@ The ``{sector_opts}`` wildcard is only used for sector-coupling studies.
|
||||
:widths: 10,20,10,10
|
||||
:file: configtables/sector-opts.csv
|
||||
|
||||
.. _scope:
|
||||
|
||||
The ``{scope}`` wildcard
|
||||
========================
|
||||
|
||||
Takes values ``residential``, ``urban``, ``total``.
|
||||
|
||||
.. _planning_horizons:
|
||||
|
||||
The ``{planning_horizons}`` wildcard
|
||||
|
||||
@ -28,7 +28,7 @@ dependencies:
|
||||
- powerplantmatching>=0.5.15
|
||||
- numpy
|
||||
- pandas>=2.1
|
||||
- geopandas>=0.11.0, <1
|
||||
- geopandas>=1
|
||||
- xarray>=2023.11.0
|
||||
- rioxarray
|
||||
- netcdf4
|
||||
|
||||
@ -202,7 +202,6 @@ rule determine_availability_matrix_MD_UA:
|
||||
+ ".nc",
|
||||
output:
|
||||
availability_matrix=resources("availability_matrix_MD-UA_{technology}.nc"),
|
||||
availability_map=resources("availability_matrix_MD-UA_{technology}.png"),
|
||||
log:
|
||||
logs("determine_availability_matrix_MD_UA_{technology}.log"),
|
||||
threads: config["atlite"].get("nprocesses", 4)
|
||||
@ -375,6 +374,37 @@ def input_conventional(w):
|
||||
}
|
||||
|
||||
|
||||
# Optional input when having Ukraine (UA) or Moldova (MD) in the countries list
|
||||
def input_gdp_pop_non_nuts3(w):
|
||||
countries = set(config_provider("countries")(w))
|
||||
if {"UA", "MD"}.intersection(countries):
|
||||
return {"gdp_pop_non_nuts3": resources("gdp_pop_non_nuts3.geojson")}
|
||||
return {}
|
||||
|
||||
|
||||
rule build_gdp_pop_non_nuts3:
|
||||
params:
|
||||
countries=config_provider("countries"),
|
||||
input:
|
||||
base_network=resources("networks/base.nc"),
|
||||
regions=resources("regions_onshore.geojson"),
|
||||
gdp_non_nuts3="data/bundle/GDP_per_capita_PPP_1990_2015_v2.nc",
|
||||
pop_non_nuts3="data/bundle/ppp_2013_1km_Aggregated.tif",
|
||||
output:
|
||||
resources("gdp_pop_non_nuts3.geojson"),
|
||||
log:
|
||||
logs("build_gdp_pop_non_nuts3.log"),
|
||||
benchmark:
|
||||
benchmarks("build_gdp_pop_non_nuts3")
|
||||
threads: 1
|
||||
resources:
|
||||
mem_mb=8000,
|
||||
conda:
|
||||
"../envs/environment.yaml"
|
||||
script:
|
||||
"../scripts/build_gdp_pop_non_nuts3.py"
|
||||
|
||||
|
||||
rule add_electricity:
|
||||
params:
|
||||
length_factor=config_provider("lines", "length_factor"),
|
||||
@ -390,6 +420,7 @@ rule add_electricity:
|
||||
input:
|
||||
unpack(input_profile_tech),
|
||||
unpack(input_conventional),
|
||||
unpack(input_gdp_pop_non_nuts3),
|
||||
base_network=resources("networks/base.nc"),
|
||||
line_rating=lambda w: (
|
||||
resources("networks/line_rating.nc")
|
||||
@ -411,7 +442,6 @@ rule add_electricity:
|
||||
),
|
||||
load=resources("electricity_demand.csv"),
|
||||
nuts3_shapes=resources("nuts3_shapes.geojson"),
|
||||
ua_md_gdp="data/GDP_PPP_30arcsec_v3_mapped_default.csv",
|
||||
output:
|
||||
resources("networks/elec.nc"),
|
||||
log:
|
||||
|
||||
@ -151,18 +151,18 @@ rule build_daily_heat_demand:
|
||||
snapshots=config_provider("snapshots"),
|
||||
drop_leap_day=config_provider("enable", "drop_leap_day"),
|
||||
input:
|
||||
pop_layout=resources("pop_layout_{scope}.nc"),
|
||||
pop_layout=resources("pop_layout_total.nc"),
|
||||
regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
|
||||
cutout=heat_demand_cutout,
|
||||
output:
|
||||
heat_demand=resources("daily_heat_demand_{scope}_elec_s{simpl}_{clusters}.nc"),
|
||||
heat_demand=resources("daily_heat_demand_total_elec_s{simpl}_{clusters}.nc"),
|
||||
resources:
|
||||
mem_mb=20000,
|
||||
threads: 8
|
||||
log:
|
||||
logs("build_daily_heat_demand_{scope}_{simpl}_{clusters}.loc"),
|
||||
logs("build_daily_heat_demand_total_{simpl}_{clusters}.loc"),
|
||||
benchmark:
|
||||
benchmarks("build_daily_heat_demand/{scope}_s{simpl}_{clusters}")
|
||||
benchmarks("build_daily_heat_demand/total_s{simpl}_{clusters}")
|
||||
conda:
|
||||
"../envs/environment.yaml"
|
||||
script:
|
||||
@ -175,16 +175,16 @@ rule build_hourly_heat_demand:
|
||||
drop_leap_day=config_provider("enable", "drop_leap_day"),
|
||||
input:
|
||||
heat_profile="data/heat_load_profile_BDEW.csv",
|
||||
heat_demand=resources("daily_heat_demand_{scope}_elec_s{simpl}_{clusters}.nc"),
|
||||
heat_demand=resources("daily_heat_demand_total_elec_s{simpl}_{clusters}.nc"),
|
||||
output:
|
||||
heat_demand=resources("hourly_heat_demand_{scope}_elec_s{simpl}_{clusters}.nc"),
|
||||
heat_demand=resources("hourly_heat_demand_total_elec_s{simpl}_{clusters}.nc"),
|
||||
resources:
|
||||
mem_mb=2000,
|
||||
threads: 8
|
||||
log:
|
||||
logs("build_hourly_heat_demand_{scope}_{simpl}_{clusters}.loc"),
|
||||
logs("build_hourly_heat_demand_total_{simpl}_{clusters}.loc"),
|
||||
benchmark:
|
||||
benchmarks("build_hourly_heat_demand/{scope}_s{simpl}_{clusters}")
|
||||
benchmarks("build_hourly_heat_demand/total_s{simpl}_{clusters}")
|
||||
conda:
|
||||
"../envs/environment.yaml"
|
||||
script:
|
||||
@ -196,19 +196,19 @@ rule build_temperature_profiles:
|
||||
snapshots=config_provider("snapshots"),
|
||||
drop_leap_day=config_provider("enable", "drop_leap_day"),
|
||||
input:
|
||||
pop_layout=resources("pop_layout_{scope}.nc"),
|
||||
pop_layout=resources("pop_layout_total.nc"),
|
||||
regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
|
||||
cutout=heat_demand_cutout,
|
||||
output:
|
||||
temp_soil=resources("temp_soil_{scope}_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_air=resources("temp_air_{scope}_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_soil=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_air=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
|
||||
resources:
|
||||
mem_mb=20000,
|
||||
threads: 8
|
||||
log:
|
||||
logs("build_temperature_profiles_{scope}_{simpl}_{clusters}.log"),
|
||||
logs("build_temperature_profiles_total_{simpl}_{clusters}.log"),
|
||||
benchmark:
|
||||
benchmarks("build_temperature_profiles/{scope}_s{simpl}_{clusters}")
|
||||
benchmarks("build_temperature_profiles/total_s{simpl}_{clusters}")
|
||||
conda:
|
||||
"../envs/environment.yaml"
|
||||
script:
|
||||
@ -220,18 +220,10 @@ rule build_cop_profiles:
|
||||
heat_pump_sink_T=config_provider("sector", "heat_pump_sink_T"),
|
||||
input:
|
||||
temp_soil_total=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_soil_rural=resources("temp_soil_rural_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_soil_urban=resources("temp_soil_urban_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_air_total=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_air_rural=resources("temp_air_rural_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_air_urban=resources("temp_air_urban_elec_s{simpl}_{clusters}.nc"),
|
||||
output:
|
||||
cop_soil_total=resources("cop_soil_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_soil_rural=resources("cop_soil_rural_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_soil_urban=resources("cop_soil_urban_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_air_total=resources("cop_air_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_air_rural=resources("cop_air_rural_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_air_urban=resources("cop_air_urban_elec_s{simpl}_{clusters}.nc"),
|
||||
resources:
|
||||
mem_mb=20000,
|
||||
log:
|
||||
@ -263,18 +255,18 @@ rule build_solar_thermal_profiles:
|
||||
drop_leap_day=config_provider("enable", "drop_leap_day"),
|
||||
solar_thermal=config_provider("solar_thermal"),
|
||||
input:
|
||||
pop_layout=resources("pop_layout_{scope}.nc"),
|
||||
pop_layout=resources("pop_layout_total.nc"),
|
||||
regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
|
||||
cutout=solar_thermal_cutout,
|
||||
output:
|
||||
solar_thermal=resources("solar_thermal_{scope}_elec_s{simpl}_{clusters}.nc"),
|
||||
solar_thermal=resources("solar_thermal_total_elec_s{simpl}_{clusters}.nc"),
|
||||
resources:
|
||||
mem_mb=20000,
|
||||
threads: 16
|
||||
log:
|
||||
logs("build_solar_thermal_profiles_{scope}_s{simpl}_{clusters}.log"),
|
||||
logs("build_solar_thermal_profiles_total_s{simpl}_{clusters}.log"),
|
||||
benchmark:
|
||||
benchmarks("build_solar_thermal_profiles/{scope}_s{simpl}_{clusters}")
|
||||
benchmarks("build_solar_thermal_profiles/total_s{simpl}_{clusters}")
|
||||
conda:
|
||||
"../envs/environment.yaml"
|
||||
script:
|
||||
@ -1024,32 +1016,14 @@ rule prepare_sector_network:
|
||||
"district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
|
||||
),
|
||||
temp_soil_total=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_soil_rural=resources("temp_soil_rural_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_soil_urban=resources("temp_soil_urban_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_air_total=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_air_rural=resources("temp_air_rural_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_air_urban=resources("temp_air_urban_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_soil_total=resources("cop_soil_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_soil_rural=resources("cop_soil_rural_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_soil_urban=resources("cop_soil_urban_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_air_total=resources("cop_air_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_air_rural=resources("cop_air_rural_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_air_urban=resources("cop_air_urban_elec_s{simpl}_{clusters}.nc"),
|
||||
solar_thermal_total=lambda w: (
|
||||
resources("solar_thermal_total_elec_s{simpl}_{clusters}.nc")
|
||||
if config_provider("sector", "solar_thermal")(w)
|
||||
else []
|
||||
),
|
||||
solar_thermal_urban=lambda w: (
|
||||
resources("solar_thermal_urban_elec_s{simpl}_{clusters}.nc")
|
||||
if config_provider("sector", "solar_thermal")(w)
|
||||
else []
|
||||
),
|
||||
solar_thermal_rural=lambda w: (
|
||||
resources("solar_thermal_rural_elec_s{simpl}_{clusters}.nc")
|
||||
if config_provider("sector", "solar_thermal")(w)
|
||||
else []
|
||||
),
|
||||
egs_potentials=lambda w: (
|
||||
resources("egs_potentials_s{simpl}_{clusters}.csv")
|
||||
if config_provider("sector", "enhanced_geothermal", "enable")(w)
|
||||
|
||||
@ -55,7 +55,7 @@ def dynamic_getter(wildcards, keys, default):
|
||||
scenario_name = wildcards.run
|
||||
if scenario_name not in scenarios:
|
||||
raise ValueError(
|
||||
f"Scenario {scenario_name} not found in file {config['run']['scenario']['file']}."
|
||||
f"Scenario {scenario_name} not found in file {config['run']['scenarios']['file']}."
|
||||
)
|
||||
config_with_scenario = scenario_config(scenario_name)
|
||||
config_with_wildcards = update_config_from_wildcards(
|
||||
@ -81,7 +81,8 @@ def config_provider(*keys, default=None):
|
||||
def solver_threads(w):
|
||||
solver_options = config_provider("solving", "solver_options")(w)
|
||||
option_set = config_provider("solving", "solver", "options")(w)
|
||||
threads = solver_options[option_set].get("threads", 4)
|
||||
solver_option_set = solver_options[option_set]
|
||||
threads = solver_option_set.get("threads") or solver_option_set.get("Threads") or 4
|
||||
return threads
|
||||
|
||||
|
||||
|
||||
@ -29,6 +29,8 @@ if config["enable"]["retrieve"] and config["enable"].get("retrieve_databundle",
|
||||
"h2_salt_caverns_GWh_per_sqkm.geojson",
|
||||
"natura/natura.tiff",
|
||||
"gebco/GEBCO_2014_2D.nc",
|
||||
"GDP_per_capita_PPP_1990_2015_v2.nc",
|
||||
"ppp_2013_1km_Aggregated.tif",
|
||||
]
|
||||
|
||||
rule retrieve_databundle:
|
||||
@ -69,7 +71,7 @@ if config["enable"]["retrieve"] and config["enable"].get("retrieve_cutout", True
|
||||
rule retrieve_cutout:
|
||||
input:
|
||||
storage(
|
||||
"https://zenodo.org/records/6382570/files/{cutout}.nc",
|
||||
"https://zenodo.org/records/12791128/files/{cutout}.nc",
|
||||
),
|
||||
output:
|
||||
protected("cutouts/" + CDIR + "{cutout}.nc"),
|
||||
@ -163,7 +165,7 @@ if config["enable"]["retrieve"]:
|
||||
rule retrieve_ship_raster:
|
||||
input:
|
||||
storage(
|
||||
"https://zenodo.org/records/10973944/files/shipdensity_global.zip",
|
||||
"https://zenodo.org/records/12760663/files/shipdensity_global.zip",
|
||||
keep_local=True,
|
||||
),
|
||||
output:
|
||||
|
||||
@ -89,10 +89,6 @@ def add_brownfield(n, n_p, year):
|
||||
# deal with gas network
|
||||
pipe_carrier = ["gas pipeline"]
|
||||
if snakemake.params.H2_retrofit:
|
||||
# drop capacities of previous year to avoid duplicating
|
||||
to_drop = n.links.carrier.isin(pipe_carrier) & (n.links.build_year != year)
|
||||
n.mremove("Link", n.links.loc[to_drop].index)
|
||||
|
||||
# subtract the already retrofitted from today's gas grid capacity
|
||||
h2_retrofitted_fixed_i = n.links[
|
||||
(n.links.carrier == "H2 pipeline retrofitted")
|
||||
@ -115,10 +111,6 @@ def add_brownfield(n, n_p, year):
|
||||
index=pipe_capacity.index
|
||||
).fillna(0)
|
||||
n.links.loc[gas_pipes_i, "p_nom"] = remaining_capacity
|
||||
else:
|
||||
new_pipes = n.links.carrier.isin(pipe_carrier) & (n.links.build_year == year)
|
||||
n.links.loc[new_pipes, "p_nom"] = 0.0
|
||||
n.links.loc[new_pipes, "p_nom_min"] = 0.0
|
||||
|
||||
|
||||
def disable_grid_expansion_if_limit_hit(n):
|
||||
|
||||
@ -287,26 +287,26 @@ def shapes_to_shapes(orig, dest):
|
||||
transfer = sparse.lil_matrix((len(dest), len(orig)), dtype=float)
|
||||
|
||||
for i, j in product(range(len(dest)), range(len(orig))):
|
||||
if orig_prepped[j].intersects(dest[i]):
|
||||
area = orig[j].intersection(dest[i]).area
|
||||
transfer[i, j] = area / dest[i].area
|
||||
if orig_prepped[j].intersects(dest.iloc[i]):
|
||||
area = orig.iloc[j].intersection(dest.iloc[i]).area
|
||||
transfer[i, j] = area / dest.iloc[i].area
|
||||
|
||||
return transfer
|
||||
|
||||
|
||||
def attach_load(n, regions, load, nuts3_shapes, ua_md_gdp, countries, scaling=1.0):
|
||||
def attach_load(
|
||||
n, regions, load, nuts3_shapes, gdp_pop_non_nuts3, countries, scaling=1.0
|
||||
):
|
||||
substation_lv_i = n.buses.index[n.buses["substation_lv"]]
|
||||
regions = gpd.read_file(regions).set_index("name").reindex(substation_lv_i)
|
||||
gdf_regions = gpd.read_file(regions).set_index("name").reindex(substation_lv_i)
|
||||
opsd_load = pd.read_csv(load, index_col=0, parse_dates=True).filter(items=countries)
|
||||
|
||||
ua_md_gdp = pd.read_csv(ua_md_gdp, dtype={"name": "str"}).set_index("name")
|
||||
|
||||
logger.info(f"Load data scaled by factor {scaling}.")
|
||||
opsd_load *= scaling
|
||||
|
||||
nuts3 = gpd.read_file(nuts3_shapes).set_index("index")
|
||||
|
||||
def upsample(cntry, group):
|
||||
def upsample(cntry, group, gdp_pop_non_nuts3):
|
||||
load = opsd_load[cntry]
|
||||
|
||||
if len(group) == 1:
|
||||
@ -325,7 +325,15 @@ def attach_load(n, regions, load, nuts3_shapes, ua_md_gdp, countries, scaling=1.
|
||||
factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n))
|
||||
if cntry in ["UA", "MD"]:
|
||||
# overwrite factor because nuts3 provides no data for UA+MD
|
||||
factors = normed(ua_md_gdp.loc[group.index, "GDP_PPP"].squeeze())
|
||||
gdp_pop_non_nuts3 = gpd.read_file(gdp_pop_non_nuts3).set_index("Bus")
|
||||
gdp_pop_non_nuts3 = gdp_pop_non_nuts3.loc[
|
||||
(gdp_pop_non_nuts3.country == cntry)
|
||||
& (gdp_pop_non_nuts3.index.isin(substation_lv_i))
|
||||
]
|
||||
factors = normed(
|
||||
0.6 * normed(gdp_pop_non_nuts3["gdp"])
|
||||
+ 0.4 * normed(gdp_pop_non_nuts3["pop"])
|
||||
)
|
||||
return pd.DataFrame(
|
||||
factors.values * load.values[:, np.newaxis],
|
||||
index=load.index,
|
||||
@ -334,8 +342,8 @@ def attach_load(n, regions, load, nuts3_shapes, ua_md_gdp, countries, scaling=1.
|
||||
|
||||
load = pd.concat(
|
||||
[
|
||||
upsample(cntry, group)
|
||||
for cntry, group in regions.geometry.groupby(regions.country)
|
||||
upsample(cntry, group, gdp_pop_non_nuts3)
|
||||
for cntry, group in gdf_regions.geometry.groupby(gdf_regions.country)
|
||||
],
|
||||
axis=1,
|
||||
)
|
||||
@ -821,7 +829,7 @@ if __name__ == "__main__":
|
||||
snakemake.input.regions,
|
||||
snakemake.input.load,
|
||||
snakemake.input.nuts3_shapes,
|
||||
snakemake.input.ua_md_gdp,
|
||||
snakemake.input.get("gdp_pop_non_nuts3"),
|
||||
params.countries,
|
||||
params.scaling_factor,
|
||||
)
|
||||
@ -844,7 +852,7 @@ if __name__ == "__main__":
|
||||
fuel_price = pd.read_csv(
|
||||
snakemake.input.fuel_price, index_col=0, header=0, parse_dates=True
|
||||
)
|
||||
fuel_price = fuel_price.reindex(n.snapshots).fillna(method="ffill")
|
||||
fuel_price = fuel_price.reindex(n.snapshots).ffill()
|
||||
else:
|
||||
fuel_price = None
|
||||
|
||||
|
||||
@ -72,6 +72,7 @@ Creates the network topology from an ENTSO-E map extract, and create Voronoi sha
|
||||
"""
|
||||
|
||||
import logging
|
||||
import warnings
|
||||
from itertools import product
|
||||
|
||||
import geopandas as gpd
|
||||
@ -85,9 +86,9 @@ import shapely.wkt
|
||||
import yaml
|
||||
from _helpers import REGION_COLS, configure_logging, get_snapshots, set_scenario_config
|
||||
from packaging.version import Version, parse
|
||||
from scipy import spatial
|
||||
from scipy.sparse import csgraph
|
||||
from shapely.geometry import LineString, Point, Polygon
|
||||
from scipy.spatial import KDTree
|
||||
from shapely.geometry import LineString, Point
|
||||
|
||||
PD_GE_2_2 = parse(pd.__version__) >= Version("2.2")
|
||||
|
||||
@ -118,7 +119,7 @@ def _find_closest_links(links, new_links, distance_upper_bound=1.5):
|
||||
querycoords = np.vstack(
|
||||
[new_links[["x1", "y1", "x2", "y2"]], new_links[["x2", "y2", "x1", "y1"]]]
|
||||
)
|
||||
tree = spatial.KDTree(treecoords)
|
||||
tree = KDTree(treecoords)
|
||||
dist, ind = tree.query(querycoords, distance_upper_bound=distance_upper_bound)
|
||||
found_b = ind < len(links)
|
||||
found_i = np.arange(len(new_links) * 2)[found_b] % len(new_links)
|
||||
@ -273,7 +274,7 @@ def _add_links_from_tyndp(buses, links, links_tyndp, europe_shape):
|
||||
return buses, links
|
||||
|
||||
tree_buses = buses.query("carrier=='AC'")
|
||||
tree = spatial.KDTree(tree_buses[["x", "y"]])
|
||||
tree = KDTree(tree_buses[["x", "y"]])
|
||||
_, ind0 = tree.query(links_tyndp[["x1", "y1"]])
|
||||
ind0_b = ind0 < len(tree_buses)
|
||||
links_tyndp.loc[ind0_b, "bus0"] = tree_buses.index[ind0[ind0_b]]
|
||||
@ -671,7 +672,7 @@ def _set_links_underwater_fraction(n, offshore_shapes):
|
||||
if not hasattr(n.links, "geometry"):
|
||||
n.links["underwater_fraction"] = 0.0
|
||||
else:
|
||||
offshore_shape = gpd.read_file(offshore_shapes).unary_union
|
||||
offshore_shape = gpd.read_file(offshore_shapes).union_all()
|
||||
links = gpd.GeoSeries(n.links.geometry.dropna().map(shapely.wkt.loads))
|
||||
n.links["underwater_fraction"] = (
|
||||
links.intersection(offshore_shape).length / links.length
|
||||
@ -788,59 +789,26 @@ def base_network(
|
||||
return n
|
||||
|
||||
|
||||
def voronoi_partition_pts(points, outline):
|
||||
def voronoi(points, outline, crs=4326):
|
||||
"""
|
||||
Compute the polygons of a voronoi partition of `points` within the polygon
|
||||
`outline`. Taken from
|
||||
https://github.com/FRESNA/vresutils/blob/master/vresutils/graph.py.
|
||||
|
||||
Attributes
|
||||
----------
|
||||
points : Nx2 - ndarray[dtype=float]
|
||||
outline : Polygon
|
||||
Returns
|
||||
-------
|
||||
polygons : N - ndarray[dtype=Polygon|MultiPolygon]
|
||||
Create Voronoi polygons from a set of points within an outline.
|
||||
"""
|
||||
points = np.asarray(points)
|
||||
pts = gpd.GeoSeries(
|
||||
gpd.points_from_xy(points.x, points.y),
|
||||
index=points.index,
|
||||
crs=crs,
|
||||
)
|
||||
voronoi = pts.voronoi_polygons(extend_to=outline).clip(outline)
|
||||
|
||||
if len(points) == 1:
|
||||
polygons = [outline]
|
||||
else:
|
||||
xmin, ymin = np.amin(points, axis=0)
|
||||
xmax, ymax = np.amax(points, axis=0)
|
||||
xspan = xmax - xmin
|
||||
yspan = ymax - ymin
|
||||
# can be removed with shapely 2.1 where order is preserved
|
||||
# https://github.com/shapely/shapely/issues/2020
|
||||
with warnings.catch_warnings():
|
||||
warnings.filterwarnings("ignore", category=UserWarning)
|
||||
pts = gpd.GeoDataFrame(geometry=pts)
|
||||
voronoi = gpd.GeoDataFrame(geometry=voronoi)
|
||||
joined = gpd.sjoin_nearest(pts, voronoi, how="right")
|
||||
|
||||
# to avoid any network positions outside all Voronoi cells, append
|
||||
# the corners of a rectangle framing these points
|
||||
vor = spatial.Voronoi(
|
||||
np.vstack(
|
||||
(
|
||||
points,
|
||||
[
|
||||
[xmin - 3.0 * xspan, ymin - 3.0 * yspan],
|
||||
[xmin - 3.0 * xspan, ymax + 3.0 * yspan],
|
||||
[xmax + 3.0 * xspan, ymin - 3.0 * yspan],
|
||||
[xmax + 3.0 * xspan, ymax + 3.0 * yspan],
|
||||
],
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
polygons = []
|
||||
for i in range(len(points)):
|
||||
poly = Polygon(vor.vertices[vor.regions[vor.point_region[i]]])
|
||||
|
||||
if not poly.is_valid:
|
||||
poly = poly.buffer(0)
|
||||
|
||||
with np.errstate(invalid="ignore"):
|
||||
poly = poly.intersection(outline)
|
||||
|
||||
polygons.append(poly)
|
||||
|
||||
return polygons
|
||||
return joined.dissolve(by="Bus").squeeze()
|
||||
|
||||
|
||||
def build_bus_shapes(n, country_shapes, offshore_shapes, countries):
|
||||
@ -870,11 +838,10 @@ def build_bus_shapes(n, country_shapes, offshore_shapes, countries):
|
||||
"name": onshore_locs.index,
|
||||
"x": onshore_locs["x"],
|
||||
"y": onshore_locs["y"],
|
||||
"geometry": voronoi_partition_pts(
|
||||
onshore_locs.values, onshore_shape
|
||||
),
|
||||
"geometry": voronoi(onshore_locs, onshore_shape),
|
||||
"country": country,
|
||||
}
|
||||
},
|
||||
crs=n.crs,
|
||||
)
|
||||
)
|
||||
|
||||
@ -887,14 +854,16 @@ def build_bus_shapes(n, country_shapes, offshore_shapes, countries):
|
||||
"name": offshore_locs.index,
|
||||
"x": offshore_locs["x"],
|
||||
"y": offshore_locs["y"],
|
||||
"geometry": voronoi_partition_pts(offshore_locs.values, offshore_shape),
|
||||
"geometry": voronoi(offshore_locs, offshore_shape),
|
||||
"country": country,
|
||||
}
|
||||
},
|
||||
crs=n.crs,
|
||||
)
|
||||
offshore_regions_c = offshore_regions_c.loc[offshore_regions_c.area > 1e-2]
|
||||
sel = offshore_regions_c.to_crs(3035).area > 10 # m2
|
||||
offshore_regions_c = offshore_regions_c.loc[sel]
|
||||
offshore_regions.append(offshore_regions_c)
|
||||
|
||||
shapes = pd.concat(onshore_regions, ignore_index=True)
|
||||
shapes = pd.concat(onshore_regions, ignore_index=True).set_crs(n.crs)
|
||||
|
||||
return onshore_regions, offshore_regions, shapes, offshore_shapes
|
||||
|
||||
|
||||
@ -21,20 +21,12 @@ Relevant Settings
|
||||
Inputs:
|
||||
-------
|
||||
- ``resources/<run_name>/temp_soil_total_elec_s<simpl>_<clusters>.nc``: Soil temperature (total) time series.
|
||||
- ``resources/<run_name>/temp_soil_rural_elec_s<simpl>_<clusters>.nc``: Soil temperature (rural) time series.
|
||||
- ``resources/<run_name>/temp_soil_urban_elec_s<simpl>_<clusters>.nc``: Soil temperature (urban) time series.
|
||||
- ``resources/<run_name>/temp_air_total_elec_s<simpl>_<clusters>.nc``: Ambient air temperature (total) time series.
|
||||
- ``resources/<run_name>/temp_air_rural_elec_s<simpl>_<clusters>.nc``: Ambient air temperature (rural) time series.
|
||||
- ``resources/<run_name>/temp_air_urban_elec_s<simpl>_<clusters>.nc``: Ambient air temperature (urban) time series.
|
||||
|
||||
Outputs:
|
||||
--------
|
||||
- ``resources/cop_soil_total_elec_s<simpl>_<clusters>.nc``: COP (ground-sourced) time series (total).
|
||||
- ``resources/cop_soil_rural_elec_s<simpl>_<clusters>.nc``: COP (ground-sourced) time series (rural).
|
||||
- ``resources/cop_soil_urban_elec_s<simpl>_<clusters>.nc``: COP (ground-sourced) time series (urban).
|
||||
- ``resources/cop_air_total_elec_s<simpl>_<clusters>.nc``: COP (air-sourced) time series (total).
|
||||
- ``resources/cop_air_rural_elec_s<simpl>_<clusters>.nc``: COP (air-sourced) time series (rural).
|
||||
- ``resources/cop_air_urban_elec_s<simpl>_<clusters>.nc``: COP (air-sourced) time series (urban).
|
||||
|
||||
|
||||
References
|
||||
@ -67,12 +59,11 @@ if __name__ == "__main__":
|
||||
|
||||
set_scenario_config(snakemake)
|
||||
|
||||
for area in ["total", "urban", "rural"]:
|
||||
for source in ["air", "soil"]:
|
||||
source_T = xr.open_dataarray(snakemake.input[f"temp_{source}_{area}"])
|
||||
for source in ["air", "soil"]:
|
||||
source_T = xr.open_dataarray(snakemake.input[f"temp_{source}_total"])
|
||||
|
||||
delta_T = snakemake.params.heat_pump_sink_T - source_T
|
||||
delta_T = snakemake.params.heat_pump_sink_T - source_T
|
||||
|
||||
cop = coefficient_of_performance(delta_T, source)
|
||||
cop = coefficient_of_performance(delta_T, source)
|
||||
|
||||
cop.to_netcdf(snakemake.output[f"cop_{source}_{area}"])
|
||||
cop.to_netcdf(snakemake.output[f"cop_{source}_total"])
|
||||
|
||||
@ -103,7 +103,7 @@ if __name__ == "__main__":
|
||||
if "snakemake" not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
|
||||
snakemake = mock_snakemake("build_cutout", cutout="europe-2013-era5")
|
||||
snakemake = mock_snakemake("build_cutout", cutout="europe-2013-sarah3-era5")
|
||||
configure_logging(snakemake)
|
||||
set_scenario_config(snakemake)
|
||||
|
||||
|
||||
@ -86,7 +86,7 @@ if __name__ == "__main__":
|
||||
urban_fraction = pd.concat([urban_fraction, dist_fraction_node], axis=1).max(axis=1)
|
||||
|
||||
# difference of max potential and today's share of district heating
|
||||
diff = (urban_fraction * central_fraction) - dist_fraction_node
|
||||
diff = ((urban_fraction * central_fraction) - dist_fraction_node).clip(lower=0)
|
||||
progress = get(
|
||||
snakemake.config["sector"]["district_heating"]["progress"], investment_year
|
||||
)
|
||||
|
||||
@ -7,6 +7,7 @@ Build import locations for fossil gas from entry-points, LNG terminals and
|
||||
production sites with data from SciGRID_gas and Global Energy Monitor.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
|
||||
import geopandas as gpd
|
||||
@ -19,7 +20,8 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
def read_scigrid_gas(fn):
|
||||
df = gpd.read_file(fn)
|
||||
df = pd.concat([df, df.param.apply(pd.Series)], axis=1)
|
||||
expanded_param = df.param.apply(json.loads).apply(pd.Series)
|
||||
df = pd.concat([df, expanded_param], axis=1)
|
||||
df.drop(["param", "uncertainty", "method"], axis=1, inplace=True)
|
||||
return df
|
||||
|
||||
@ -97,11 +99,11 @@ def build_gas_input_locations(gem_fn, entry_fn, sto_fn, countries):
|
||||
~(entry.from_country.isin(countries) & entry.to_country.isin(countries))
|
||||
& ~entry.name.str.contains("Tegelen") # only take non-EU entries
|
||||
| (entry.from_country == "NO") # malformed datapoint # entries from NO to GB
|
||||
]
|
||||
].copy()
|
||||
|
||||
sto = read_scigrid_gas(sto_fn)
|
||||
remove_country = ["RU", "UA", "TR", "BY"] # noqa: F841
|
||||
sto = sto.query("country_code not in @remove_country")
|
||||
sto = sto.query("country_code not in @remove_country").copy()
|
||||
|
||||
# production sites inside the model scope
|
||||
prod = build_gem_prod_data(gem_fn)
|
||||
@ -132,7 +134,8 @@ if __name__ == "__main__":
|
||||
snakemake = mock_snakemake(
|
||||
"build_gas_input_locations",
|
||||
simpl="",
|
||||
clusters="128",
|
||||
clusters="5",
|
||||
configfiles="config/test/config.overnight.yaml",
|
||||
)
|
||||
|
||||
configure_logging(snakemake)
|
||||
@ -162,7 +165,7 @@ if __name__ == "__main__":
|
||||
|
||||
gas_input_nodes = gpd.sjoin(gas_input_locations, regions, how="left")
|
||||
|
||||
gas_input_nodes.rename(columns={"index_right": "bus"}, inplace=True)
|
||||
gas_input_nodes.rename(columns={"name": "bus"}, inplace=True)
|
||||
|
||||
gas_input_nodes.to_file(snakemake.output.gas_input_nodes, driver="GeoJSON")
|
||||
|
||||
|
||||
@ -7,6 +7,7 @@ Preprocess gas network based on data from bthe SciGRID_gas project
|
||||
(https://www.gas.scigrid.de/).
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
|
||||
import geopandas as gpd
|
||||
@ -54,8 +55,9 @@ def diameter_to_capacity(pipe_diameter_mm):
|
||||
|
||||
def load_dataset(fn):
|
||||
df = gpd.read_file(fn)
|
||||
param = df.param.apply(pd.Series)
|
||||
method = df.method.apply(pd.Series)[["diameter_mm", "max_cap_M_m3_per_d"]]
|
||||
param = df.param.apply(json.loads).apply(pd.Series)
|
||||
cols = ["diameter_mm", "max_cap_M_m3_per_d"]
|
||||
method = df.method.apply(json.loads).apply(pd.Series)[cols]
|
||||
method.columns = method.columns + "_method"
|
||||
df = pd.concat([df, param, method], axis=1)
|
||||
to_drop = ["param", "uncertainty", "method", "tags"]
|
||||
|
||||
153
scripts/build_gdp_pop_non_nuts3.py
Normal file
153
scripts/build_gdp_pop_non_nuts3.py
Normal file
@ -0,0 +1,153 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# SPDX-FileCopyrightText: : 2017-2024 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: MIT
|
||||
"""
|
||||
Maps the per-capita GDP and population values to non-NUTS3 regions.
|
||||
|
||||
The script takes as input the country code, a GeoDataFrame containing
|
||||
the regions, and the file paths to the datasets containing the GDP and
|
||||
POP values for non-NUTS3 countries.
|
||||
"""
|
||||
|
||||
import logging
|
||||
|
||||
import geopandas as gpd
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pypsa
|
||||
import rasterio
|
||||
import xarray as xr
|
||||
from _helpers import configure_logging, set_scenario_config
|
||||
from rasterio.mask import mask
|
||||
from shapely.geometry import box
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def calc_gdp_pop(country, regions, gdp_non_nuts3, pop_non_nuts3):
|
||||
"""
|
||||
Calculate the GDP p.c. and population values for non NUTS3 regions.
|
||||
|
||||
Parameters:
|
||||
country (str): The two-letter country code of the non-NUTS3 region.
|
||||
regions (GeoDataFrame): A GeoDataFrame containing the regions.
|
||||
gdp_non_nuts3 (str): The file path to the dataset containing the GDP p.c values
|
||||
for non NUTS3 countries (e.g. MD, UA)
|
||||
pop_non_nuts3 (str): The file path to the dataset containing the POP values
|
||||
for non NUTS3 countries (e.g. MD, UA)
|
||||
|
||||
Returns:
|
||||
tuple: A tuple containing two GeoDataFrames:
|
||||
- gdp: A GeoDataFrame with the mean GDP p.c. values mapped to each bus.
|
||||
- pop: A GeoDataFrame with the summed POP values mapped to each bus.
|
||||
"""
|
||||
regions = (
|
||||
regions.rename(columns={"name": "Bus"})
|
||||
.drop(columns=["x", "y"])
|
||||
.set_index("Bus")
|
||||
)
|
||||
regions = regions[regions.country == country]
|
||||
# Create a bounding box for UA, MD from region shape, including a buffer of 10000 metres
|
||||
bounding_box = (
|
||||
gpd.GeoDataFrame(geometry=[box(*regions.total_bounds)], crs=regions.crs)
|
||||
.to_crs(epsg=3857)
|
||||
.buffer(10000)
|
||||
.to_crs(regions.crs)
|
||||
)
|
||||
|
||||
# GDP Mapping
|
||||
logger.info(f"Mapping mean GDP p.c. to non-NUTS3 region: {country}")
|
||||
with xr.open_dataset(gdp_non_nuts3) as src_gdp:
|
||||
src_gdp = src_gdp.where(
|
||||
(src_gdp.longitude >= bounding_box.bounds.minx.min())
|
||||
& (src_gdp.longitude <= bounding_box.bounds.maxx.max())
|
||||
& (src_gdp.latitude >= bounding_box.bounds.miny.min())
|
||||
& (src_gdp.latitude <= bounding_box.bounds.maxy.max()),
|
||||
drop=True,
|
||||
)
|
||||
gdp = src_gdp.to_dataframe().reset_index()
|
||||
gdp = gdp.rename(columns={"GDP_per_capita_PPP": "gdp"})
|
||||
gdp = gdp[gdp.time == gdp.time.max()]
|
||||
gdp_raster = gpd.GeoDataFrame(
|
||||
gdp,
|
||||
geometry=gpd.points_from_xy(gdp.longitude, gdp.latitude),
|
||||
crs="EPSG:4326",
|
||||
)
|
||||
gdp_mapped = gpd.sjoin(gdp_raster, regions, predicate="within")
|
||||
gdp = (
|
||||
gdp_mapped.copy()
|
||||
.groupby(["Bus", "country"])
|
||||
.agg({"gdp": "mean"})
|
||||
.reset_index(level=["country"])
|
||||
)
|
||||
|
||||
# Population Mapping
|
||||
logger.info(f"Mapping summed population to non-NUTS3 region: {country}")
|
||||
with rasterio.open(pop_non_nuts3) as src_pop:
|
||||
# Mask the raster with the bounding box
|
||||
out_image, out_transform = mask(src_pop, bounding_box, crop=True)
|
||||
out_meta = src_pop.meta.copy()
|
||||
out_meta.update(
|
||||
{
|
||||
"driver": "GTiff",
|
||||
"height": out_image.shape[1],
|
||||
"width": out_image.shape[2],
|
||||
"transform": out_transform,
|
||||
}
|
||||
)
|
||||
masked_data = out_image[0] # Use the first band (rest is empty)
|
||||
row_indices, col_indices = np.where(masked_data != src_pop.nodata)
|
||||
values = masked_data[row_indices, col_indices]
|
||||
|
||||
# Affine transformation from pixel coordinates to geo coordinates
|
||||
x_coords, y_coords = rasterio.transform.xy(out_transform, row_indices, col_indices)
|
||||
pop_raster = pd.DataFrame({"x": x_coords, "y": y_coords, "pop": values})
|
||||
pop_raster = gpd.GeoDataFrame(
|
||||
pop_raster,
|
||||
geometry=gpd.points_from_xy(pop_raster.x, pop_raster.y),
|
||||
crs=src_pop.crs,
|
||||
)
|
||||
pop_mapped = gpd.sjoin(pop_raster, regions, predicate="within")
|
||||
pop = (
|
||||
pop_mapped.groupby(["Bus", "country"])
|
||||
.agg({"pop": "sum"})
|
||||
.reset_index()
|
||||
.set_index("Bus")
|
||||
)
|
||||
gdp_pop = regions.join(gdp.drop(columns="country"), on="Bus").join(
|
||||
pop.drop(columns="country"), on="Bus"
|
||||
)
|
||||
gdp_pop.fillna(0, inplace=True)
|
||||
|
||||
return gdp_pop
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if "snakemake" not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
|
||||
snakemake = mock_snakemake("build_gdp_pop_non_nuts3")
|
||||
configure_logging(snakemake)
|
||||
set_scenario_config(snakemake)
|
||||
|
||||
n = pypsa.Network(snakemake.input.base_network)
|
||||
regions = gpd.read_file(snakemake.input.regions)
|
||||
|
||||
gdp_non_nuts3 = snakemake.input.gdp_non_nuts3
|
||||
pop_non_nuts3 = snakemake.input.pop_non_nuts3
|
||||
|
||||
subset = {"MD", "UA"}.intersection(snakemake.params.countries)
|
||||
|
||||
gdp_pop = pd.concat(
|
||||
[
|
||||
calc_gdp_pop(country, regions, gdp_non_nuts3, pop_non_nuts3)
|
||||
for country in subset
|
||||
],
|
||||
axis=0,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"Exporting GDP and POP values for non-NUTS3 regions {snakemake.output}"
|
||||
)
|
||||
gdp_pop.reset_index().to_file(snakemake.output, driver="GeoJSON")
|
||||
@ -22,12 +22,12 @@ Inputs
|
||||
------
|
||||
|
||||
- ``data/heat_load_profile_BDEW.csv``: Intraday heat profile for water and space heating demand for the residential and services sectors for weekends and weekdays.
|
||||
- ``resources/daily_heat_demand_<scope>_elec_s<simpl>_<clusters>.nc``: Daily heat demand per cluster.
|
||||
- ``resources/daily_heat_demand_total_elec_s<simpl>_<clusters>.nc``: Daily heat demand per cluster.
|
||||
|
||||
Outputs
|
||||
-------
|
||||
|
||||
- ``resources/hourly_heat_demand_<scope>_elec_s<simpl>_<clusters>.nc``:
|
||||
- ``resources/hourly_heat_demand_total_elec_s<simpl>_<clusters>.nc``:
|
||||
"""
|
||||
|
||||
from itertools import product
|
||||
@ -41,10 +41,10 @@ if __name__ == "__main__":
|
||||
from _helpers import mock_snakemake
|
||||
|
||||
snakemake = mock_snakemake(
|
||||
"build_hourly_heat_demands",
|
||||
"build_hourly_heat_demand",
|
||||
scope="total",
|
||||
simpl="",
|
||||
clusters=48,
|
||||
clusters=5,
|
||||
)
|
||||
set_scenario_config(snakemake)
|
||||
|
||||
@ -85,6 +85,6 @@ if __name__ == "__main__":
|
||||
|
||||
heat_demand.index.name = "snapshots"
|
||||
|
||||
ds = heat_demand.stack().to_xarray()
|
||||
ds = heat_demand.stack(future_stack=True).to_xarray()
|
||||
|
||||
ds.to_netcdf(snakemake.output.heat_demand)
|
||||
|
||||
@ -116,7 +116,7 @@ def prepare_hotmaps_database(regions):
|
||||
|
||||
gdf = gpd.sjoin(gdf, regions, how="inner", predicate="within")
|
||||
|
||||
gdf.rename(columns={"index_right": "bus"}, inplace=True)
|
||||
gdf.rename(columns={"name": "bus"}, inplace=True)
|
||||
gdf["country"] = gdf.bus.str[:2]
|
||||
|
||||
# the .sjoin can lead to duplicates if a geom is in two overlapping regions
|
||||
|
||||
@ -184,7 +184,7 @@ def separate_basic_chemicals(demand, production):
|
||||
|
||||
demand.drop(columns="Basic chemicals", inplace=True)
|
||||
|
||||
demand["HVC"].clip(lower=0, inplace=True)
|
||||
demand["HVC"] = demand["HVC"].clip(lower=0)
|
||||
|
||||
return demand
|
||||
|
||||
@ -248,7 +248,7 @@ if __name__ == "__main__":
|
||||
demand = add_non_eu28_industrial_energy_demand(countries, demand, production)
|
||||
|
||||
# for format compatibility
|
||||
demand = demand.stack(dropna=False).unstack(level=[0, 2])
|
||||
demand = demand.stack(future_stack=True).unstack(level=[0, 2])
|
||||
|
||||
# style and annotation
|
||||
demand.index.name = "TWh/a"
|
||||
|
||||
@ -301,7 +301,8 @@ def separate_basic_chemicals(demand, year):
|
||||
demand["Basic chemicals"] -= demand["Ammonia"]
|
||||
|
||||
# EE, HR and LT got negative demand through subtraction - poor data
|
||||
demand["Basic chemicals"].clip(lower=0.0, inplace=True)
|
||||
col = "Basic chemicals"
|
||||
demand[col] = demand[col].clip(lower=0.0)
|
||||
|
||||
# assume HVC, methanol, chlorine production proportional to non-ammonia basic chemicals
|
||||
distribution_key = (
|
||||
|
||||
@ -129,11 +129,12 @@ def build_industry_sector_ratios_intermediate():
|
||||
]
|
||||
today_sector_ratios_ct.loc[:, ~missing_mask] = today_sector_ratios_ct.loc[
|
||||
:, ~missing_mask
|
||||
].fillna(0)
|
||||
].fillna(future_sector_ratios)
|
||||
intermediate_sector_ratios[ct] = (
|
||||
today_sector_ratios_ct * (1 - fraction_future)
|
||||
+ future_sector_ratios * fraction_future
|
||||
)
|
||||
|
||||
intermediate_sector_ratios = pd.concat(intermediate_sector_ratios, axis=1)
|
||||
|
||||
intermediate_sector_ratios.to_csv(snakemake.output.industry_sector_ratios)
|
||||
|
||||
@ -92,7 +92,9 @@ if __name__ == "__main__":
|
||||
|
||||
# The first low density grid cells to reach rural fraction are rural
|
||||
asc_density_i = density_cells_ct.sort_values().index
|
||||
asc_density_cumsum = pop_cells_ct[asc_density_i].cumsum() / pop_cells_ct.sum()
|
||||
asc_density_cumsum = (
|
||||
pop_cells_ct.iloc[asc_density_i].cumsum() / pop_cells_ct.sum()
|
||||
)
|
||||
rural_fraction_ct = 1 - urban_fraction[ct]
|
||||
pop_ct_rural_b = asc_density_cumsum < rural_fraction_ct
|
||||
pop_ct_urban_b = ~pop_ct_rural_b
|
||||
|
||||
@ -6,7 +6,7 @@
|
||||
# coding: utf-8
|
||||
"""
|
||||
Retrieves conventional powerplant capacities and locations from
|
||||
`powerplantmatching <https://github.com/FRESNA/powerplantmatching>`_, assigns
|
||||
`powerplantmatching <https://github.com/PyPSA/powerplantmatching>`_, assigns
|
||||
these to buses and creates a ``.csv`` file. It is possible to amend the
|
||||
powerplant database with custom entries provided in
|
||||
``data/custom_powerplants.csv``.
|
||||
@ -30,17 +30,17 @@ Inputs
|
||||
------
|
||||
|
||||
- ``networks/base.nc``: confer :ref:`base`.
|
||||
- ``data/custom_powerplants.csv``: custom powerplants in the same format as `powerplantmatching <https://github.com/FRESNA/powerplantmatching>`_ provides
|
||||
- ``data/custom_powerplants.csv``: custom powerplants in the same format as `powerplantmatching <https://github.com/PyPSA/powerplantmatching>`_ provides
|
||||
|
||||
Outputs
|
||||
-------
|
||||
|
||||
- ``resource/powerplants.csv``: A list of conventional power plants (i.e. neither wind nor solar) with fields for name, fuel type, technology, country, capacity in MW, duration, commissioning year, retrofit year, latitude, longitude, and dam information as documented in the `powerplantmatching README <https://github.com/FRESNA/powerplantmatching/blob/master/README.md>`_; additionally it includes information on the closest substation/bus in ``networks/base.nc``.
|
||||
- ``resource/powerplants.csv``: A list of conventional power plants (i.e. neither wind nor solar) with fields for name, fuel type, technology, country, capacity in MW, duration, commissioning year, retrofit year, latitude, longitude, and dam information as documented in the `powerplantmatching README <https://github.com/PyPSA/powerplantmatching/blob/master/README.md>`_; additionally it includes information on the closest substation/bus in ``networks/base.nc``.
|
||||
|
||||
.. image:: img/powerplantmatching.png
|
||||
:scale: 30 %
|
||||
|
||||
**Source:** `powerplantmatching on GitHub <https://github.com/FRESNA/powerplantmatching>`_
|
||||
**Source:** `powerplantmatching on GitHub <https://github.com/PyPSA/powerplantmatching>`_
|
||||
|
||||
Description
|
||||
-----------
|
||||
|
||||
@ -406,7 +406,7 @@ if __name__ == "__main__":
|
||||
|
||||
if snakemake.wildcards.technology.startswith("offwind"):
|
||||
logger.info("Calculate underwater fraction of connections.")
|
||||
offshore_shape = gpd.read_file(snakemake.input["offshore_shapes"]).unary_union
|
||||
offshore_shape = gpd.read_file(snakemake.input["offshore_shapes"]).union_all()
|
||||
underwater_fraction = []
|
||||
for bus in buses:
|
||||
p = centre_of_mass.sel(bus=bus).data
|
||||
|
||||
@ -890,7 +890,7 @@ def calculate_gain_utilisation_factor(heat_transfer_perm2, Q_ht, Q_gain):
|
||||
Calculates gain utilisation factor nu.
|
||||
"""
|
||||
# time constant of the building tau [h] = c_m [Wh/(m^2K)] * 1 /(H_tr_e+H_tb*H_ve) [m^2 K /W]
|
||||
tau = c_m / heat_transfer_perm2.T.groupby(axis=1).sum().T
|
||||
tau = c_m / heat_transfer_perm2.groupby().sum()
|
||||
alpha = alpha_H_0 + (tau / tau_H_0)
|
||||
# heat balance ratio
|
||||
gamma = (1 / Q_ht).mul(Q_gain.sum(axis=1), axis=0)
|
||||
|
||||
@ -91,7 +91,7 @@ def _get_country(target, **keys):
|
||||
return np.nan
|
||||
|
||||
|
||||
def _simplify_polys(polys, minarea=0.1, tolerance=0.01, filterremote=True):
|
||||
def _simplify_polys(polys, minarea=0.1, tolerance=None, filterremote=True):
|
||||
if isinstance(polys, MultiPolygon):
|
||||
polys = sorted(polys.geoms, key=attrgetter("area"), reverse=True)
|
||||
mainpoly = polys[0]
|
||||
@ -106,7 +106,9 @@ def _simplify_polys(polys, minarea=0.1, tolerance=0.01, filterremote=True):
|
||||
)
|
||||
else:
|
||||
polys = mainpoly
|
||||
return polys.simplify(tolerance=tolerance)
|
||||
if tolerance is not None:
|
||||
polys = polys.simplify(tolerance=tolerance)
|
||||
return polys
|
||||
|
||||
|
||||
def countries(naturalearth, country_list):
|
||||
@ -124,7 +126,7 @@ def countries(naturalearth, country_list):
|
||||
df = df.loc[
|
||||
df.name.isin(country_list) & ((df["scalerank"] == 0) | (df["scalerank"] == 5))
|
||||
]
|
||||
s = df.set_index("name")["geometry"].map(_simplify_polys)
|
||||
s = df.set_index("name")["geometry"].map(_simplify_polys).set_crs(df.crs)
|
||||
if "RS" in country_list:
|
||||
s["RS"] = s["RS"].union(s.pop("KV"))
|
||||
# cleanup shape union
|
||||
@ -145,7 +147,8 @@ def eez(country_shapes, eez, country_list):
|
||||
lambda s: _simplify_polys(s, filterremote=False)
|
||||
)
|
||||
s = gpd.GeoSeries(
|
||||
{k: v for k, v in s.items() if v.distance(country_shapes[k]) < 1e-3}
|
||||
{k: v for k, v in s.items() if v.distance(country_shapes[k]) < 1e-3},
|
||||
crs=df.crs,
|
||||
)
|
||||
s = s.to_frame("geometry")
|
||||
s.index.name = "name"
|
||||
@ -156,7 +159,7 @@ def country_cover(country_shapes, eez_shapes=None):
|
||||
shapes = country_shapes
|
||||
if eez_shapes is not None:
|
||||
shapes = pd.concat([shapes, eez_shapes])
|
||||
europe_shape = shapes.unary_union
|
||||
europe_shape = shapes.union_all()
|
||||
if isinstance(europe_shape, MultiPolygon):
|
||||
europe_shape = max(europe_shape.geoms, key=attrgetter("area"))
|
||||
return Polygon(shell=europe_shape.exterior)
|
||||
@ -235,11 +238,11 @@ def nuts3(country_shapes, nuts3, nuts3pop, nuts3gdp, ch_cantons, ch_popgdp):
|
||||
[["BA1", "BA", 3871.0], ["RS1", "RS", 7210.0], ["AL1", "AL", 2893.0]],
|
||||
columns=["NUTS_ID", "country", "pop"],
|
||||
geometry=gpd.GeoSeries(),
|
||||
crs=df.crs,
|
||||
)
|
||||
manual["geometry"] = manual["country"].map(country_shapes)
|
||||
manual["geometry"] = manual["country"].map(country_shapes.to_crs(df.crs))
|
||||
manual = manual.dropna()
|
||||
manual = manual.set_index("NUTS_ID")
|
||||
manual = manual.set_crs("ETRS89")
|
||||
|
||||
df = pd.concat([df, manual], sort=False)
|
||||
|
||||
@ -265,7 +268,8 @@ if __name__ == "__main__":
|
||||
offshore_shapes.reset_index().to_file(snakemake.output.offshore_shapes)
|
||||
|
||||
europe_shape = gpd.GeoDataFrame(
|
||||
geometry=[country_cover(country_shapes, offshore_shapes.geometry)]
|
||||
geometry=[country_cover(country_shapes, offshore_shapes.geometry)],
|
||||
crs=country_shapes.crs,
|
||||
)
|
||||
europe_shape.reset_index().to_file(snakemake.output.europe_shape)
|
||||
|
||||
|
||||
@ -45,9 +45,7 @@ if __name__ == "__main__":
|
||||
# assign ports to nearest region
|
||||
p = european_ports.to_crs(3857)
|
||||
r = regions.to_crs(3857)
|
||||
outflows = (
|
||||
p.sjoin_nearest(r).groupby("index_right").properties_outflows.sum().div(1e3)
|
||||
)
|
||||
outflows = p.sjoin_nearest(r).groupby("name").properties_outflows.sum().div(1e3)
|
||||
|
||||
# calculate fraction of each country's port outflows
|
||||
countries = outflows.index.str[:2]
|
||||
|
||||
@ -25,15 +25,15 @@ Relevant Settings
|
||||
Inputs
|
||||
------
|
||||
|
||||
- ``resources/<run_name>/pop_layout_<scope>.nc``:
|
||||
- ``resources/<run_name>/pop_layout_total.nc``:
|
||||
- ``resources/<run_name>/regions_onshore_elec_s<simpl>_<clusters>.geojson``:
|
||||
- ``cutout``: Weather data cutout, as specified in config
|
||||
|
||||
Outputs
|
||||
-------
|
||||
|
||||
- ``resources/temp_soil_<scope>_elec_s<simpl>_<clusters>.nc``:
|
||||
- ``resources/temp_air_<scope>_elec_s<simpl>_<clusters>.nc`
|
||||
- ``resources/temp_soil_total_elec_s<simpl>_<clusters>.nc``:
|
||||
- ``resources/temp_air_total_elec_s<simpl>_<clusters>.nc`
|
||||
"""
|
||||
|
||||
import atlite
|
||||
|
||||
@ -41,9 +41,9 @@ def build_clustered_gas_network(df, bus_regions, length_factor=1.25):
|
||||
for i in [0, 1]:
|
||||
gdf = gpd.GeoDataFrame(geometry=df[f"point{i}"], crs="EPSG:4326")
|
||||
|
||||
bus_mapping = gpd.sjoin(
|
||||
gdf, bus_regions, how="left", predicate="within"
|
||||
).index_right
|
||||
bus_mapping = gpd.sjoin(gdf, bus_regions, how="left", predicate="within")[
|
||||
"name"
|
||||
]
|
||||
bus_mapping = bus_mapping.groupby(bus_mapping.index).first()
|
||||
|
||||
df[f"bus{i}"] = bus_mapping
|
||||
@ -58,6 +58,9 @@ def build_clustered_gas_network(df, bus_regions, length_factor=1.25):
|
||||
# drop pipes within the same region
|
||||
df = df.loc[df.bus1 != df.bus0]
|
||||
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
# recalculate lengths as center to center * length factor
|
||||
df["length"] = df.apply(
|
||||
lambda p: length_factor
|
||||
|
||||
@ -8,16 +8,15 @@ Create land elibility analysis for Ukraine and Moldova with different datasets.
|
||||
|
||||
import functools
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from tempfile import NamedTemporaryFile
|
||||
|
||||
import atlite
|
||||
import fiona
|
||||
import geopandas as gpd
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from _helpers import configure_logging, set_scenario_config
|
||||
from atlite.gis import shape_availability
|
||||
from rasterio.plot import show
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -40,7 +39,7 @@ if __name__ == "__main__":
|
||||
configure_logging(snakemake)
|
||||
set_scenario_config(snakemake)
|
||||
|
||||
nprocesses = None # snakemake.config["atlite"].get("nprocesses")
|
||||
nprocesses = int(snakemake.threads)
|
||||
noprogress = not snakemake.config["atlite"].get("show_progress", True)
|
||||
config = snakemake.config["renewable"][snakemake.wildcards.technology]
|
||||
|
||||
@ -48,7 +47,9 @@ if __name__ == "__main__":
|
||||
regions = (
|
||||
gpd.read_file(snakemake.input.regions).set_index("name").rename_axis("bus")
|
||||
)
|
||||
buses = regions.index
|
||||
# Limit to "UA" and "MD" regions
|
||||
buses = regions.loc[regions["country"].isin(["UA", "MD"])].index.values
|
||||
regions = regions.loc[buses]
|
||||
|
||||
excluder = atlite.ExclusionContainer(crs=3035, res=100)
|
||||
|
||||
@ -93,8 +94,15 @@ if __name__ == "__main__":
|
||||
bbox=regions.geometry,
|
||||
layer=layer,
|
||||
).to_crs(3035)
|
||||
|
||||
# temporary file needed for parallelization
|
||||
with NamedTemporaryFile(suffix=".geojson", delete=False) as f:
|
||||
plg_tmp_fn = f.name
|
||||
if not wdpa.empty:
|
||||
excluder.add_geometry(wdpa.geometry)
|
||||
wdpa[["geometry"]].to_file(plg_tmp_fn)
|
||||
while not os.path.exists(plg_tmp_fn):
|
||||
time.sleep(1)
|
||||
excluder.add_geometry(plg_tmp_fn)
|
||||
|
||||
layer = get_wdpa_layer_name(wdpa_fn, "points")
|
||||
wdpa_pts = gpd.read_file(
|
||||
@ -107,8 +115,15 @@ if __name__ == "__main__":
|
||||
wdpa_pts = wdpa_pts.set_geometry(
|
||||
wdpa_pts["geometry"].buffer(wdpa_pts["buffer_radius"])
|
||||
)
|
||||
|
||||
# temporary file needed for parallelization
|
||||
with NamedTemporaryFile(suffix=".geojson", delete=False) as f:
|
||||
pts_tmp_fn = f.name
|
||||
if not wdpa_pts.empty:
|
||||
excluder.add_geometry(wdpa_pts.geometry)
|
||||
wdpa_pts[["geometry"]].to_file(pts_tmp_fn)
|
||||
while not os.path.exists(pts_tmp_fn):
|
||||
time.sleep(1)
|
||||
excluder.add_geometry(pts_tmp_fn)
|
||||
|
||||
if "max_depth" in config:
|
||||
# lambda not supported for atlite + multiprocessing
|
||||
@ -144,16 +159,10 @@ if __name__ == "__main__":
|
||||
else:
|
||||
availability = cutout.availabilitymatrix(regions, excluder, **kwargs)
|
||||
|
||||
regions_geometry = regions.to_crs(3035).geometry
|
||||
band, transform = shape_availability(regions_geometry, excluder)
|
||||
fig, ax = plt.subplots(figsize=(4, 8))
|
||||
gpd.GeoSeries(regions_geometry.unary_union).plot(ax=ax, color="none")
|
||||
show(band, transform=transform, cmap="Greens", ax=ax)
|
||||
plt.axis("off")
|
||||
plt.savefig(snakemake.output.availability_map, bbox_inches="tight", dpi=500)
|
||||
for fn in [pts_tmp_fn, plg_tmp_fn]:
|
||||
if os.path.exists(fn):
|
||||
os.remove(fn)
|
||||
|
||||
# Limit results only to buses for UA and MD
|
||||
buses = regions.loc[regions["country"].isin(["UA", "MD"])].index.values
|
||||
availability = availability.sel(bus=buses)
|
||||
|
||||
# Save and plot for verification
|
||||
|
||||
@ -631,7 +631,7 @@ def calculate_co2_emissions(n, label, df):
|
||||
weightings = n.snapshot_weightings.generators.mul(
|
||||
n.investment_period_weightings["years"]
|
||||
.reindex(n.snapshots)
|
||||
.fillna(method="bfill")
|
||||
.bfill()
|
||||
.fillna(1.0),
|
||||
axis=0,
|
||||
)
|
||||
|
||||
@ -70,7 +70,7 @@ if __name__ == "__main__":
|
||||
optimized = optimized[["Generator", "StorageUnit"]].droplevel(0, axis=1)
|
||||
optimized = optimized.rename(columns=n.buses.country, level=0)
|
||||
optimized = optimized.rename(columns=carrier_groups, level=1)
|
||||
optimized = optimized.groupby(axis=1, level=[0, 1]).sum()
|
||||
optimized = optimized.T.groupby(level=[0, 1]).sum().T
|
||||
|
||||
data = pd.concat([historic, optimized], keys=["Historic", "Optimized"], axis=1)
|
||||
data.columns.names = ["Kind", "Country", "Carrier"]
|
||||
|
||||
@ -137,9 +137,7 @@ def add_emission_prices(n, emission_prices={"co2": 0.0}, exclude_co2=False):
|
||||
def add_dynamic_emission_prices(n):
|
||||
co2_price = pd.read_csv(snakemake.input.co2_price, index_col=0, parse_dates=True)
|
||||
co2_price = co2_price[~co2_price.index.duplicated()]
|
||||
co2_price = (
|
||||
co2_price.reindex(n.snapshots).fillna(method="ffill").fillna(method="bfill")
|
||||
)
|
||||
co2_price = co2_price.reindex(n.snapshots).ffill().bfill()
|
||||
|
||||
emissions = (
|
||||
n.generators.carrier.map(n.carriers.co2_emissions) / n.generators.efficiency
|
||||
|
||||
@ -250,7 +250,7 @@ def adjust_stores(n):
|
||||
n.stores.loc[cyclic_i, "e_cyclic_per_period"] = True
|
||||
n.stores.loc[cyclic_i, "e_cyclic"] = False
|
||||
# non cyclic store assumptions
|
||||
non_cyclic_store = ["co2", "co2 stored", "solid biomass", "biogas", "Li ion"]
|
||||
non_cyclic_store = ["co2", "co2 stored", "solid biomass", "biogas", "EV battery"]
|
||||
co2_i = n.stores[n.stores.carrier.isin(non_cyclic_store)].index
|
||||
n.stores.loc[co2_i, "e_cyclic_per_period"] = False
|
||||
n.stores.loc[co2_i, "e_cyclic"] = False
|
||||
|
||||
@ -548,14 +548,17 @@ def add_carrier_buses(n, carrier, nodes=None):
|
||||
capital_cost=capital_cost,
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Generator",
|
||||
nodes,
|
||||
bus=nodes,
|
||||
p_nom_extendable=True,
|
||||
carrier=carrier,
|
||||
marginal_cost=costs.at[carrier, "fuel"],
|
||||
)
|
||||
fossils = ["coal", "gas", "oil", "lignite"]
|
||||
if options.get("fossil_fuels", True) and carrier in fossils:
|
||||
|
||||
n.madd(
|
||||
"Generator",
|
||||
nodes,
|
||||
bus=nodes,
|
||||
p_nom_extendable=True,
|
||||
carrier=carrier,
|
||||
marginal_cost=costs.at[carrier, "fuel"],
|
||||
)
|
||||
|
||||
|
||||
# TODO: PyPSA-Eur merge issue
|
||||
@ -694,6 +697,7 @@ def add_co2_tracking(n, costs, options):
|
||||
e_nom_extendable=True,
|
||||
e_nom_max=e_nom_max,
|
||||
capital_cost=options["co2_sequestration_cost"],
|
||||
marginal_cost=-0.1,
|
||||
bus=sequestration_buses,
|
||||
lifetime=options["co2_sequestration_lifetime"],
|
||||
carrier="co2 sequestered",
|
||||
@ -1243,12 +1247,14 @@ def add_storage_and_grids(n, costs):
|
||||
gas_pipes["p_nom_min"] = 0.0
|
||||
# 0.1 EUR/MWkm/a to prefer decommissioning to address degeneracy
|
||||
gas_pipes["capital_cost"] = 0.1 * gas_pipes.length
|
||||
gas_pipes["p_nom_extendable"] = True
|
||||
else:
|
||||
gas_pipes["p_nom_max"] = np.inf
|
||||
gas_pipes["p_nom_min"] = gas_pipes.p_nom
|
||||
gas_pipes["capital_cost"] = (
|
||||
gas_pipes.length * costs.at["CH4 (g) pipeline", "fixed"]
|
||||
)
|
||||
gas_pipes["p_nom_extendable"] = False
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
@ -1257,14 +1263,14 @@ def add_storage_and_grids(n, costs):
|
||||
bus1=gas_pipes.bus1 + " gas",
|
||||
p_min_pu=gas_pipes.p_min_pu,
|
||||
p_nom=gas_pipes.p_nom,
|
||||
p_nom_extendable=True,
|
||||
p_nom_extendable=gas_pipes.p_nom_extendable,
|
||||
p_nom_max=gas_pipes.p_nom_max,
|
||||
p_nom_min=gas_pipes.p_nom_min,
|
||||
length=gas_pipes.length,
|
||||
capital_cost=gas_pipes.capital_cost,
|
||||
tags=gas_pipes.name,
|
||||
carrier="gas pipeline",
|
||||
lifetime=costs.at["CH4 (g) pipeline", "lifetime"],
|
||||
lifetime=np.inf,
|
||||
)
|
||||
|
||||
# remove fossil generators where there is neither
|
||||
@ -1546,14 +1552,14 @@ def add_EVs(
|
||||
temperature,
|
||||
):
|
||||
|
||||
n.add("Carrier", "Li ion")
|
||||
n.add("Carrier", "EV battery")
|
||||
|
||||
n.madd(
|
||||
"Bus",
|
||||
spatial.nodes,
|
||||
suffix=" EV battery",
|
||||
location=spatial.nodes,
|
||||
carrier="Li ion",
|
||||
carrier="EV battery",
|
||||
unit="MWh_el",
|
||||
)
|
||||
|
||||
@ -1626,9 +1632,9 @@ def add_EVs(
|
||||
n.madd(
|
||||
"Store",
|
||||
spatial.nodes,
|
||||
suffix=" battery storage",
|
||||
suffix=" EV battery",
|
||||
bus=spatial.nodes + " EV battery",
|
||||
carrier="battery storage",
|
||||
carrier="EV battery",
|
||||
e_cyclic=True,
|
||||
e_nom=e_nom,
|
||||
e_max_pu=1,
|
||||
@ -2833,10 +2839,11 @@ def add_industry(n, costs):
|
||||
)
|
||||
|
||||
domestic_navigation = pop_weighted_energy_totals.loc[
|
||||
nodes, "total domestic navigation"
|
||||
nodes, ["total domestic navigation"]
|
||||
].squeeze()
|
||||
international_navigation = (
|
||||
pd.read_csv(snakemake.input.shipping_demand, index_col=0).squeeze() * nyears
|
||||
pd.read_csv(snakemake.input.shipping_demand, index_col=0).squeeze(axis=1)
|
||||
* nyears
|
||||
)
|
||||
all_navigation = domestic_navigation + international_navigation
|
||||
p_set = all_navigation * 1e6 / nhours
|
||||
@ -2955,7 +2962,7 @@ def add_industry(n, costs):
|
||||
carrier="oil",
|
||||
)
|
||||
|
||||
if "oil" not in n.generators.carrier.unique():
|
||||
if options.get("fossil_fuels", True) and "oil" not in n.generators.carrier.unique():
|
||||
n.madd(
|
||||
"Generator",
|
||||
spatial.oil.nodes,
|
||||
@ -3422,7 +3429,7 @@ def add_waste_heat(n):
|
||||
)
|
||||
n.links.loc[urban_central + " Fischer-Tropsch", "efficiency3"] = (
|
||||
0.95 - n.links.loc[urban_central + " Fischer-Tropsch", "efficiency"]
|
||||
)
|
||||
) * options["use_fischer_tropsch_waste_heat"]
|
||||
|
||||
if options["use_methanation_waste_heat"] and "Sabatier" in link_carriers:
|
||||
n.links.loc[urban_central + " Sabatier", "bus3"] = (
|
||||
@ -3430,7 +3437,7 @@ def add_waste_heat(n):
|
||||
)
|
||||
n.links.loc[urban_central + " Sabatier", "efficiency3"] = (
|
||||
0.95 - n.links.loc[urban_central + " Sabatier", "efficiency"]
|
||||
)
|
||||
) * options["use_methanation_waste_heat"]
|
||||
|
||||
# DEA quotes 15% of total input (11% of which are high-value heat)
|
||||
if options["use_haber_bosch_waste_heat"] and "Haber-Bosch" in link_carriers:
|
||||
@ -3447,7 +3454,7 @@ def add_waste_heat(n):
|
||||
)
|
||||
n.links.loc[urban_central + " Haber-Bosch", "efficiency3"] = (
|
||||
0.15 * total_energy_input / electricity_input
|
||||
)
|
||||
) * options["use_haber_bosch_waste_heat"]
|
||||
|
||||
if (
|
||||
options["use_methanolisation_waste_heat"]
|
||||
@ -3459,11 +3466,11 @@ def add_waste_heat(n):
|
||||
n.links.loc[urban_central + " methanolisation", "efficiency4"] = (
|
||||
costs.at["methanolisation", "heat-output"]
|
||||
/ costs.at["methanolisation", "hydrogen-input"]
|
||||
)
|
||||
) * options["use_methanolisation_waste_heat"]
|
||||
|
||||
# TODO integrate usable waste heat efficiency into technology-data from DEA
|
||||
if (
|
||||
options.get("use_electrolysis_waste_heat", False)
|
||||
options["use_electrolysis_waste_heat"]
|
||||
and "H2 Electrolysis" in link_carriers
|
||||
):
|
||||
n.links.loc[urban_central + " H2 Electrolysis", "bus2"] = (
|
||||
@ -3471,7 +3478,7 @@ def add_waste_heat(n):
|
||||
)
|
||||
n.links.loc[urban_central + " H2 Electrolysis", "efficiency2"] = (
|
||||
0.84 - n.links.loc[urban_central + " H2 Electrolysis", "efficiency"]
|
||||
)
|
||||
) * options["use_electrolysis_waste_heat"]
|
||||
|
||||
if options["use_fuel_cell_waste_heat"] and "H2 Fuel Cell" in link_carriers:
|
||||
n.links.loc[urban_central + " H2 Fuel Cell", "bus2"] = (
|
||||
@ -3479,7 +3486,7 @@ def add_waste_heat(n):
|
||||
)
|
||||
n.links.loc[urban_central + " H2 Fuel Cell", "efficiency2"] = (
|
||||
0.95 - n.links.loc[urban_central + " H2 Fuel Cell", "efficiency"]
|
||||
)
|
||||
) * options["use_fuel_cell_waste_heat"]
|
||||
|
||||
|
||||
def add_agriculture(n, costs):
|
||||
@ -3790,7 +3797,7 @@ def lossy_bidirectional_links(n, carrier, efficiencies={}):
|
||||
rev_links.index = rev_links.index.map(lambda x: x + "-reversed")
|
||||
|
||||
n.links = pd.concat([n.links, rev_links], sort=False)
|
||||
n.links["reversed"] = n.links["reversed"].fillna(False)
|
||||
n.links["reversed"] = n.links["reversed"].fillna(False).infer_objects(copy=False)
|
||||
n.links["length_original"] = n.links["length_original"].fillna(n.links.length)
|
||||
|
||||
# do compression losses after concatenation to take electricity consumption at bus0 in either direction
|
||||
@ -4015,12 +4022,11 @@ if __name__ == "__main__":
|
||||
|
||||
snakemake = mock_snakemake(
|
||||
"prepare_sector_network",
|
||||
# configfiles="test/config.overnight.yaml",
|
||||
simpl="",
|
||||
opts="",
|
||||
clusters="37",
|
||||
ll="v1.0",
|
||||
sector_opts="730H-T-H-B-I-A-dist1",
|
||||
clusters="1",
|
||||
ll="vopt",
|
||||
sector_opts="",
|
||||
planning_horizons="2050",
|
||||
)
|
||||
|
||||
|
||||
@ -48,7 +48,7 @@ if __name__ == "__main__":
|
||||
configure_logging(snakemake)
|
||||
set_scenario_config(snakemake)
|
||||
|
||||
url = "https://zenodo.org/records/10973944/files/bundle.tar.xz"
|
||||
url = "https://zenodo.org/records/12760663/files/bundle.tar.xz"
|
||||
|
||||
tarball_fn = Path(f"{rootpath}/bundle.tar.xz")
|
||||
to_fn = Path(rootpath) / Path(snakemake.output[0]).parent.parent
|
||||
|
||||
@ -28,7 +28,8 @@ if __name__ == "__main__":
|
||||
|
||||
disable_progress = snakemake.config["run"].get("disable_progressbar", False)
|
||||
url_eurostat = (
|
||||
"https://ec.europa.eu/eurostat/documents/38154/4956218/Balances-April2023.zip"
|
||||
# "https://ec.europa.eu/eurostat/documents/38154/4956218/Balances-April2023.zip" # link down
|
||||
"https://tubcloud.tu-berlin.de/s/prkJpL7B9M3cDPb/download/Balances-April2023.zip"
|
||||
)
|
||||
tarball_fn = Path(f"{rootpath}/data/eurostat/eurostat_2023.zip")
|
||||
to_fn = Path(f"{rootpath}/data/eurostat/Balances-April2023/")
|
||||
|
||||
@ -155,7 +155,7 @@ def _add_land_use_constraint(n):
|
||||
existing_large, "p_nom_min"
|
||||
]
|
||||
|
||||
n.generators.p_nom_max.clip(lower=0, inplace=True)
|
||||
n.generators["p_nom_max"] = n.generators["p_nom_max"].clip(lower=0)
|
||||
|
||||
|
||||
def _add_land_use_constraint_m(n, planning_horizons, config):
|
||||
@ -207,7 +207,7 @@ def _add_land_use_constraint_m(n, planning_horizons, config):
|
||||
existing_large, "p_nom_min"
|
||||
]
|
||||
|
||||
n.generators.p_nom_max.clip(lower=0, inplace=True)
|
||||
n.generators["p_nom_max"] = n.generators["p_nom_max"].clip(lower=0)
|
||||
|
||||
|
||||
def add_solar_potential_constraints(n, config):
|
||||
@ -471,6 +471,22 @@ def prepare_network(
|
||||
p_nom=1e9, # kW
|
||||
)
|
||||
|
||||
if solve_opts.get("curtailment_mode"):
|
||||
n.add("Carrier", "curtailment", color="#fedfed", nice_name="Curtailment")
|
||||
n.generators_t.p_min_pu = n.generators_t.p_max_pu
|
||||
buses_i = n.buses.query("carrier == 'AC'").index
|
||||
n.madd(
|
||||
"Generator",
|
||||
buses_i,
|
||||
suffix=" curtailment",
|
||||
bus=buses_i,
|
||||
p_min_pu=-1,
|
||||
p_max_pu=0,
|
||||
marginal_cost=-0.1,
|
||||
carrier="curtailment",
|
||||
p_nom=1e6,
|
||||
)
|
||||
|
||||
if solve_opts.get("noisy_costs"):
|
||||
for t in n.iterate_components():
|
||||
# if 'capital_cost' in t.df:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user